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ICLR.cc/2022/Conference | Efficient Bi-level Optimization for Non-smooth Optimization | Bi-level optimization plays a key role in a lot of machine learning applications. However, existing state-of-the-art bi-level optimization methods are limited to smooth or some specific non-smooth lower-level problems. Even worse, most of them depend on approximating hypergradients to update upper-level variable which is the inherent reason for non-efficiency. Currently, achieving a generalized and efficient optimization algorithm for bi-level problems with a non-smooth, even non-Lipschitz continuous lower-level objective is still an open question to the best of our knowledge. To address these challenging problems, in this paper, we propose a new bi-level optimization algorithm based on the smoothing and penalty techniques. Specifically, we first produce a sequence of smoothed lower-level objectives with an exponential decay smoothing parameter for the non-smooth lower-level problem. Then, we transform the smoothed bi-level optimization to an unconstrained penalty problem by replacing the smoothed sub-problem with its first-order necessary conditions. Finally, we update the upper and lower-level variables alternately with doubly stochastic gradients of the unconstrained penalty problem. Importantly, we provide the theoretical analysis to show that our method can converge to a stationary point of original non-smooth bi-level problem if the lower-level problem is convex, and we give the necessary condition of the original problem if the lower-level problem is nonconvex. We compare our method with existing state-of-the-art bi-level optimization methods in three tasks, and all the experimental results demonstrate that our method is superior to the others in terms of accuracy and efficiency. | Withdrawn |
ICLR.cc/2023/Conference | Tree-structure segmentation for logistic regression | The decision for a financial institution to accept or deny a loan is based on the probability of a client paying back their debt in time. This probability is given by a model such as a logistic regression, and estimated based on, e.g., the clients’ characteristics, their credit history, the repayment performance. Historically, different models have been developed on different markets and/or credit products and/or addressed population. We show that this amounts to modelling default as a mixture model composed of a decision tree and logistic regression on its leaves (thereafter “logistic regression tree”). We seek to optimise this practice by considering the population to which a client belongs as a latent variable, which we will estimate. After exposing the context, the notations and the problem formalisation, we will conduct estimation using a Stochastic-Expectation-Maximisation (SEM) algorithm. We will finally show the performance on simulated data, and on real retail credit data from [COMPANY], as well as real open-source data. | Reject |
ICLR.cc/2023/Conference | Generalization Bounds with Arbitrary Complexity Measures | In statistical learning theory, generalization bounds usually involve a complexity measure that is constrained by the considered theoretical framework. This limits the scope of such analysis, as in practical algorithms, other forms of regularization are used. Indeed, the empirical work of Jiang et al. (2019) shows that (I) common complexity measures (such as the VC-dimension) do not correlate with the generalization gap and that (ii) there exist arbitrary complexity measures that are better correlated with the generalization gap, but come without generalization guarantees. In this paper, we bridge the gap between this line of empirical works and generalization bounds of statistical learning theory. To do so, we leverage the framework of disintegrated PAC-Bayes bounds to derive a generalization bound that involves an arbitrary complexity measure. Our bound stands in probability jointly over the hypotheses and the learning sample, which allows us to improve the correlation between generalization gap and complexity, as the latter can be set to fit both the hypothesis class and the task. | Reject |
ICLR.cc/2023/Conference | Heterogeneous Continual Learning | We propose a novel framework and a solution to tackle the continual learning (CL) problem with progressive evolution of neural networks. Most CL methods focus on adapting a single network to a new task/class by modifying its weights. However, with rapid progress in architecture design, the problem of adapting existing solutions to novel architectures becomes relevant. For the first time, we propose Heterogeneous Continual Learning (HCL) to address this problem, where a wide range of evolving network architectures emerge continually together with novel data/tasks. As a solution, we build on top of the distillation family of techniques and modify it to a new setting where a weaker model takes the role of a teacher; meanwhile, a new stronger architecture acts as a student. Furthermore, we consider a setup of limited access to previous data and propose Quick Deep Inversion (QDI) to recover prior task visual features to support knowledge transfer. QDI significantly reduces computational costs compared to previous solutions and improves overall performance. In summary, we propose a new setup for CL with a modified knowledge distillation paradigm and design a quick data inversion method to enhance distillation. Our evaluation of various benchmarks shows that the proposed method can successfully progress over various networks while outperforming state-of-the-art methods with a 2x improvement on accuracy. | Withdrawn |
ICLR.cc/2021/Conference | PriorityCut: Occlusion-aware Regularization for Image Animation | Image animation generates a video of a source image following the motion of a driving video. Self-supervised image animation approaches do not require explicit pose references as inputs, thus offering large flexibility in learning. State-of-the-art self-supervised image animation approaches mostly warp the source image according to the motion of the driving video, and recover the warping artifacts by inpainting. When the source and the driving images have large pose differences, heavy inpainting is necessary. Without guidance, heavily inpainted regions usually suffer from loss of details. While previous data augmentation techniques such as CutMix are effective in regularizing non-warp-based image generation, directly applying them to image animation ignores the difficulty of inpainting on the warped image. We propose PriorityCut, a novel augmentation approach that uses the top-$k$ percent occluded pixels of the foreground to regularize image animation. By taking into account the difficulty of inpainting, PriorityCut preserves better identity than vanilla CutMix and outperforms state-of-the-art image animation models in terms of the pixel-wise difference, low-level similarity, keypoint distance, and feature embedding distance. | Reject |
ICLR.cc/2023/Conference | Language models are multilingual chain-of-thought reasoners | We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al., 2021) into ten typologically diverse languages. We find that the ability to solve MGSM problems via chain-of-thought prompting emerges with increasing model scale, and that models have strikingly strong multilingual reasoning abilities, even in underrepresented languages such as Bengali and Swahili. Finally, we show that multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment. The MGSM benchmark is publicly available at AnonymousLink and the supplementary material.
| Accept: poster |
ICLR.cc/2023/Conference | On Explaining Neural Network Robustness with Activation Path | Despite their verified performance, neural networks are prone to be misled by maliciously designed adversarial examples. This work investigates the robustness of neural networks from the activation pattern perspective. We find that despite the complex structure of the deep neural network, most of the neurons provide locally stable contributions to the output, while the minority, which we refer to as float neurons, can greatly affect the prediction. We decompose the computational graph of the neural network into the fixed path and float path and investigate their role in generating adversarial examples. Based on our analysis, we categorize the vulnerable examples into Lipschitz vulnerability and float neuron vulnerability. We show that the boost of robust accuracy from randomized smoothing is the result of correcting the latter. We then propose an SC-RFP (smoothed classifier with repressed float path) to further reduce the instability of the float neurons and show that our result can provide a higher certified radius as well as accuracy. | Accept: poster |
ICLR.cc/2020/Conference | Deep Nonlinear Stochastic Optimal Control for Systems with Multiplicative Uncertainties | We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations. Such PDEs arise when one considers stochastic dynamics characterized by uncertainties that are additive and control multiplicative. Stochastic models with the aforementioned characteristics have been used in computational neuroscience, biology, finance and aerospace systems and provide a more accurate representation of actuation than models with additive uncertainty. Previous literature has established the inadequacy of the linear HJB theory and instead rely on a non-linear Feynman-Kac lemma resulting in a second order forward-backward stochastic differential equations representation. However, the proposed solutions that use this representation suffer from compounding errors and computational complexity leading to lack of scalability. In this paper, we propose a deep learning based algorithm that leverages the second order Forward-Backward SDE representation and LSTM based recurrent neural networks to not only solve such Stochastic Optimal Control problems but also overcome the problems faced by previous approaches and scales well to high dimensional systems. The resulting control algorithm is tested on non-linear systems in robotics and biomechanics to demonstrate feasibility and out-performance against previous methods. | Reject |
ICLR.cc/2023/Conference | AUGMENTING ZERO-SHOT DENSE RETRIEVERS WITH PLUG-IN MIXTURE-OF-MEMORIES | In this paper we improve the zero-shot generalization ability of language models via Mixture-Of-Memory Augmentation (MoMA), a mechanism that retrieves augmentation documents from multiple information corpora (“external memories”), with the option to “plug in” new memory at inference time. We develop a joint learning mechanism that trains the augmentation component with latent labels derived from the end retrieval task, paired with hard negatives from the memory mixture. We instantiate the model in a zero-shot dense retrieval setting by augmenting a strong T5-based retriever with MoMA. Our model, MoMA-DR, obtains strong zero-shot retrieval accuracy on the eighteen tasks included in the standard BEIR benchmark. It outperforms other dense retrieval models of similar scales and achieves comparable accuracy with systems that seek generalization from increased scales in encoder models or vector indices. Our analysis illustrates the necessity of augmenting with mixture-of-memory for robust generalization, the benefits of joint learning, and how MoMA-DR utilizes the plug-in memory at inference time without changing its parameters. We plan to open source our code. | Reject |
ICLR.cc/2022/Conference | Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training | The recent trend of using large-scale deep neural networks (DNN) to boost performance has propelled the development of the parallel pipelining technique for efficient DNN training, which has resulted in the development of several prominent pipelines such as GPipe, PipeDream, and PipeDream-2BW. However, the current leading pipeline PipeDream-2BW still suffers from two major drawbacks, i.e., the excessive memory redundancy and the delayed weight updates across all stages. In this work, we propose a novel pipeline named WPipe, which achieves better memory efficiency and fresher weight updates. WPipe uses a novel pipelining scheme that divides model partitions into two groups. It moves the forward pass of the next period of weight updates to the front of the backward pass of the current period of weight updates in the first group, retains the order in the second group, and updates each group alternatively. This scheme can eliminate half of the delayed gradients and memory redundancy compared to PipeDream-2BW. The experiments, which train large BERT language models, show that compared to PipeDream-2BW, WPipe achieves $1.4\times$ acceleration and reduces the memory footprint by 36%, without nearly sacrificing any final model accuracy. | Accept (Poster) |
ICLR.cc/2023/Conference | (LA)YER-NEIGH(BOR) SAMPLING: DEFUSING NEIGHBORHOOD EXPLOSION | Graph Neural Networks have recently received a significant attention, however, training them at a large scale still remains as a challenge.
Minibatch training coupled with sampling is used to alleviate this challenge.
However existing approaches either suffer from the neighborhood explosion phenomenon or does not have good performance.
To deal with these issues, we propose a new sampling algorithm called LAyer-neighBOR sampling (LABOR).
It is designed to be a direct replacement for Neighborhood Sampling with the same fanout hyperparameter while sampling much fewer vertices, without sacrificing quality.
By design, the variance of the estimator of each vertex matches Neighbor Sampling from the point of view from a single vertex.
In our experiments, we demonstrate the superiority of our approach when it comes to model convergence behaviour against Neighbor Sampling and also the other Layer Sampling approaches under the same limited vertex sampling budget constraints. | Reject |
ICLR.cc/2020/Conference | Preventing Imitation Learning with Adversarial Policy Ensembles | Imitation learning can reproduce policies by observing experts, which poses a problem regarding policy propriety. Policies, such as human, or policies on deployed robots, can all be cloned without consent from the owners. How can we protect our proprietary policies from cloning by an external observer? To answer this question we introduce a new reinforcement learning framework, where we train an ensemble of optimal policies, whose demonstrations are guaranteed to be useless for an external observer. We formulate this idea by a constrained optimization problem, where the objective is to improve proprietary policies, and at the same time deteriorate the virtual policy of an eventual external observer. We design a tractable algorithm to solve this new optimization problem by modifying the standard policy gradient algorithm. It appears such problem formulation admits plausible interpretations of confidentiality, adversarial behaviour, which enables a broader perspective of this work. We demonstrate explicitly the existence of such 'non-clonable' ensembles, providing a solution to the above optimization problem, which is calculated by our modified policy gradient algorithm. To our knowledge, this is the first work regarding the protection and privacy of policies in Reinforcement Learning. | Reject |
ICLR.cc/2021/Conference | PDE-Driven Spatiotemporal Disentanglement | A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory. Following this direction, we propose in this article a novel and general paradigm for this task based on a resolution method for partial differential equations: the separation of variables. This inspiration allows us to introduce a dynamical interpretation of spatiotemporal disentanglement. It induces a principled model based on learning disentangled spatial and temporal representations of a phenomenon to accurately predict future observations. We experimentally demonstrate the performance and broad applicability of our method against prior state-of-the-art models on physical and synthetic video datasets. | Accept (Poster) |
ICLR.cc/2022/Conference | Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes | In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory. The online setting has an additional requirement of maintaining a valid solution at any point in time. For solving these new problems, we propose algorithms with theoretical guarantees, evaluate them on several real-world datasets, and show that they give comparable performance to state-of-the-art offline algorithms that store the entire data in memory and take multiple passes over it. | Reject |
ICLR.cc/2021/Conference | DeepLTRS: A Deep Latent Recommender System based on User Ratings and Reviews | We introduce a deep latent recommender system named deepLTRS in order to provide users with high quality recommendations based on observed user ratings and texts of product reviews. The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information. The addition of review information can alleviate data sparsity, thereby enhancing the predictive ability of the model. Our approach adopts a variational auto-encoder architecture as a generative deep latent variable model for both an ordinal matrix encoding users scores about products, and a document-term matrix encoding the reviews. Moreover, different from unique user-based or item-based models, deepLTRS assumes latent representations for both users and products. An alternated user/product mini-batching optimization structure is proposed to jointly capture user and product preferences. Numerical experiments on simulated and real-world data sets demonstrate that deepLTRS outperforms the state-of-the-art, in particular in contexts of extreme data sparsity. | Withdrawn |
ICLR.cc/2020/Conference | SDGM: Sparse Bayesian Classifier Based on a Discriminative Gaussian Mixture Model | In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability. Nevertheless, it is difficult to determine the number of components. We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM). In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning. This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components. The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner. Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity. Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN. | Reject |
ICLR.cc/2022/Conference | Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning | Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate (Fodor & McLaughlin, 1990; Fodor et al., 1988) between connectionist and classicist, wherein the latter continually advocates an algebraic treatment in cognitive architectures. In this work, we follow the classicist's call and propose a hybrid approach to improve systematic generalization in reasoning. Specifically, we showcase a prototype with algebraic representation for the abstract spatial-temporal reasoning task of Raven’s Progressive Matrices (RPM) and present the ALgebra-Aware Neuro- Semi-Symbolic (ALANS) learner. The ALANS learner is motivated by abstract algebra and the representation theory. It consists of a neural visual perception frontend and an algebraic abstract reasoning backend: the frontend summarizes the visual information from object-based representation, while the backend transforms it into an algebraic structure and induces the hidden operator on the fly. The induced operator is later executed to predict the answer's representation, and the choice most similar to the prediction is selected as the solution. Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization. We further show that the algebraic representation learned can be decoded by isomorphism and used to generate an answer. | Withdrawn |
ICLR.cc/2023/Conference | MANSA: LEARNING FAST AND SLOW IN MULTI-AGENT SYSTEMS WITH A GLOBAL SWITCHING AGENT | In multi-agent systems, independent learners (IL) often show remarkable performance and easily scale with the number of agents. Yet, training IL can sometimes be inefficient particularly in states that require coordinated exploration. Using observations of other agents’ actions through centralised learning (CL) enables agents to quickly learn how to coordinate their behaviour but employing CL at all states is prohibitively expensive in many real-world applications. Besides, applying CL often needs strong representational constraints (such as individual-global-max condition) that can lead to poor performance if violated. In this paper, we introduce a novel IL framework named MANSA that selectively employs CL only at states that require coordination. Central to MANSA is the additional reinforcement learning (RL) agent that uses switching controls to quickly learn when and where to activate CL so as to boost the performance of IL while using only IL everywhere else. Our theory proves that MANSA’s switching control mechanism, which can seamlessly adopt any existing multi-agent RL (MARL) algorithms, preserves MARL convergence properties in cooperative settings. Importantly, we prove that MANSA can improve performance and maximise performance given a limited budget of CL calls. We show empirically in Level-based Foraging and SMAC settings that MANSA achieves fast, superior training performance through its minimal selective use of CL. | Withdrawn |
ICLR.cc/2022/Conference | Fast Model Editing at Scale | While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers and end users of such models to correct inaccurate outputs while leaving the model otherwise intact is desirable. However, the distributed, black-box nature of the representations learned by large neural networks makes producing such targeted edits difficult. If presented with only a single problematic input and new desired output, fine-tuning approaches tend to overfit; other editing algorithms are either computationally infeasible or simply ineffective when applied to very large models. To enable easy post-hoc editing at scale, we propose Model Editor Networks using Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model's behavior. MEND learns to transform the gradient obtained by standard fine-tuning, using a low-rank decomposition of the gradient to make the parameterization of this transformation tractable. MEND can be trained on a single GPU in less than a day even for 10 billion+ parameter models; once trained MEND enables rapid application of new edits to the pre-trained model. Our experiments with T5, GPT, BERT, and BART models show that MEND is the only approach to model editing that effectively edits the behavior of models with more than 10 billion parameters. Code available at https://sites.google.com/view/mend-editing. | Accept (Poster) |
ICLR.cc/2021/Conference | Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games | Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first reward. To tackle this problem, a common approach is to use reward shaping to help exploration. However, an important drawback of reward shaping is that agents sometimes learn to optimize the shaped reward instead of the true objective. In this paper, we present a novel technique that we call action guidance that successfully trains agents to eventually optimize the true objective in games with sparse rewards while maintaining most of the sample efficiency that comes with reward shaping. We evaluate our approach in a simplified real-time strategy (RTS) game simulator called $\mu$RTS. | Reject |
ICLR.cc/2023/Conference | Pre-train Graph Neural Networks for Brain Network Analysis | Human brains, controlling behaviors and cognition, are at the center of complex neurobiological systems. Recent studies in neuroscience and neuroimaging analysis have reached a consensus that interactions among brain regions of interest (ROIs) are driving factors for neural development and disorders. Graph neural networks as a powerful tool for analyzing graph-structured data are naturally applied to the analysis of brain networks. However, training of deep learning models including GNNs often requires a significant amount of labeled data. Due to the complicated data acquisition process and restrictions on data sharing, brain network datasets are still small compared to other domains (e.g., molecules, proteins). Moreover, real clinical tasks (e.g., mental disorder analysis) are often conducted on local datasets with even smaller scales and larger noises. To this end, we propose to leverage pre-training to capture the intrinsic brain network structures regardless of specific clinical outcomes. Specifically, we characterize the contributions in this work from two perspectives: (1) We design brain-network-oriented unsupervised pre-training techniques to utilize large-scale brain imaging studies without highly relevant task labels. (2) To facilitate effective knowledge transfer across studies with different ROI systems, we propose to develop a data-driven parcellation atlas mapping pipeline. The proposed pre-training techniques are validated with various GNN models. Extensive experiments demonstrate consistent improvement in performance as well as robustness. | Reject |
ICLR.cc/2019/Conference | PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention | A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry. However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds. This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned from given semantic contexts. Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points. Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations. Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation. Further, conditional PointGrow learns a smooth manifold of given images where 3D shape interpolation and arithmetic calculation can be performed inside. | Withdrawn |
ICLR.cc/2020/Conference | Augmenting Transformers with KNN-Based Composite Memory | Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augmenting Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialogue modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge from Wikipedia, images, and human-written dialogue utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation. | Reject |
ICLR.cc/2021/Conference | Intra-layer Neural Architecture Search | We propose an efficient neural architecture search (NAS) algorithm with a flexible search space that encompasses layer operations down to individual weights. This work addresses NAS challenges in a search space of weight connections within layers, specifically the large number of architecture variations compared to a high-level search space with predetermined layer types. Our algorithm continuously evolves network architecture by adding new candidate parameters (weights and biases) using a first-order estimation based on their gradients at 0. Training is decoupled into alternating steps: adjusting network weights holding architecture constant, and adjusting network architecture holding weights constant. We explore additional applications by extend this method for multi-task learning with shared parameters. On the CIFAR-10 dataset, our evolved network achieves an accuracy of 97.42\% with 5M parameters, and 93.75\% with 500K parameters. On the ImageNet dataset, we achieve 76.6\% top-1 and 92.5\% top-5 accuracy with a search restriction of 8.5M parameters. | Withdrawn |
ICLR.cc/2022/Conference | G-Mixup: Graph Augmentation for Graph Classification | This work develops \emph{mixup to graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels of random two samples. Traditionally, Mixup can operate on regular, grid-like, and Euclidean data such as image or tabular data. However, it is challenging to directly adopt Mixup to augment graph data because two graphs typically: 1) have different numbers of nodes; 2) are not readily aligned; and 3) have unique topologies in non-Euclidean space. To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i.e., graphon) of different classes of graphs. Specifically, we first use graphs within the same class to estimate a graphon. Then, instead of directly manipulating graphs, we interpolate graphons of different classes in the Euclidean space to get mixed graphons, where the synthetic graphs are generated through sampling based on the new graphons. | Reject |
ICLR.cc/2020/Conference | Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling | Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently. However, learning from demonstrations often suffers from the covariate shift problem, which results
in cascading errors of the learned policy. We introduce a notion of conservatively extrapolated value functions, which provably lead to policies with self-correction. We design an algorithm Value Iteration with Negative Sampling (VINS) that practically learns such value functions with conservative extrapolation. We show that VINS can correct mistakes of the behavioral cloning policy on simulated robotics benchmark tasks. We also propose the algorithm of using VINS to initialize a reinforcement learning algorithm, which is shown to outperform prior works in sample efficiency. | Accept (Poster) |
ICLR.cc/2019/Conference | A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks | Currently available survival analysis methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks. This paper develops a novel deep learning architecture that flexibly incorporates the available longitudinal data comprising various repeated measurements (rather than only the last available measurements) in order to issue dynamically updated survival predictions for one or multiple competing risk(s). Unlike existing works in the survival analysis on the basis of longitudinal data, the proposed method learns the time-to-event distributions without specifying underlying stochastic assumptions of the longitudinal or the time-to-event processes. Thus, our method is able to learn associations between the longitudinal data and the various associated risks in a fully data-driven fashion. We demonstrate the power of our method by applying it to real-world longitudinal datasets and show a drastic improvement over state-of-the-art methods in discriminative performance. Furthermore, our analysis of the variable importance and dynamic survival predictions will yield a better understanding of the predicted risks which will result in more effective health care. | Reject |
ICLR.cc/2021/Conference | Learning Energy-Based Models by Diffusion Recovery Likelihood | While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained with recovery likelihood, which maximizes the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. Optimizing recovery likelihood is more tractable than marginal likelihood, as sampling from the conditional distributions is much easier than sampling from the marginal distributions. After training, synthesized images can be generated by the sampling process that initializes from Gaussian white noise distribution and progressively samples the conditional distributions at decreasingly lower noise levels. Our method generates high fidelity samples on various image datasets. On unconditional CIFAR-10 our method achieves FID 9.58 and inception score 8.30, superior to the majority of GANs. Moreover, we demonstrate that unlike previous work on EBMs, our long-run MCMC samples from the conditional distributions do not diverge and still represent realistic images, allowing us to accurately estimate the normalized density of data even for high-dimensional datasets. Our implementation is available at \url{https://github.com/ruiqigao/recovery_likelihood}.
| Accept (Poster) |
ICLR.cc/2022/Conference | Assessing two novel distance-based loss functions for few-shot image classification | Few-shot learning is a challenging area of research which aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches benefit from the meta-learning process in which we have episodic tasks conformed by support set (training) and query set (test), and the objective is to learn a similarity comparison metric between those sets. Due to the lack of data, the learning process of the embedding network becomes an important part of the few-shot task. In this work, we propose two different loss functions which consider the importance of the embedding vectors by looking at the intra-class and inter-class distance between the few data. The first loss function is the Proto-Triplet Loss, which is based on the original triplet loss with the modifications needed to better work on few-shot scenarios. The second loss function is based on an inter and intra class nearest neighbors score, which help us to know the quality of embeddings obtained from the trained network. Extensive experimental results on the miniImagenNet benchmark increase the accuracy performance from other metric-based few-shot learning methods by a margin of $2\%$, demonstrating the capability of these loss functions to allow the network to generalize better to previously unseen classes. | Reject |
ICLR.cc/2020/Conference | Multi-Agent Interactions Modeling with Correlated Policies | In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents. However, previous work on modeling multi-agent interactions from demonstrations is primarily constrained by assuming the independence among policies and their reward structures.
In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents’ policies, which can recover agents' policies that can regenerate similar interactions. Consequently, we develop a Decentralized Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL), which allows for decentralized training and execution. Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators and outperforms state-of-the-art multi-agent imitation learning methods. Our code is available at \url{https://github.com/apexrl/CoDAIL}. | Accept (Poster) |
ICLR.cc/2019/Conference | Towards GAN Benchmarks Which Require Generalization | For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic.
We clarify a necessary condition for an evaluation metric not to behave this way: estimating the function must require a large sample from the model. In search of such a metric, we turn to neural network divergences (NNDs), which are defined in terms of a neural network trained to distinguish between distributions. The resulting benchmarks cannot be ``won'' by training set memorization, while still being perceptually correlated and computable only from samples. We survey past work on using NNDs for evaluation, implement an example black-box metric based on these ideas, and validate experimentally that it can measure a notion of generalization.
| Accept (Poster) |
ICLR.cc/2018/Conference | Maintaining cooperation in complex social dilemmas using deep reinforcement learning | Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare. Building artificially intelligent agents that achieve good outcomes in these situations is important because many real world interactions include a tension between selfish interests and the welfare of others. We show how to modify modern reinforcement learning methods to construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (try to return to mutual cooperation). We show both theoretically and experimentally that such agents can maintain cooperation in Markov social dilemmas. Our construction does not require training methods beyond a modification of self-play, thus if an environment is such that good strategies can be constructed in the zero-sum case (eg. Atari) then we can construct agents that solve social dilemmas in this environment. | Reject |
ICLR.cc/2020/Conference | Privacy-preserving Representation Learning by Disentanglement | Deep learning and latest machine learning technology heralded an era of success in data analysis. Accompanied by the ever increasing performance, reaching super-human performance in many areas, is the requirement of amassing more and more data to train these models. Often ignored or underestimated, the big data curation is associated with the risk of privacy leakages. The proposed approach seeks to mitigate these privacy issues. In order to sanitize data from sensitive content, we propose to learn a privacy-preserving data representation by disentangling into public and private part, with the public part being shareable without privacy infringement. The proposed approach deals with the setting where the private features are not explicit, and is estimated though the course of learning. This is particularly appealing, when the notion of sensitive attribute is ``fuzzy''. We showcase feasibility in terms of classification of facial attributes and identity on the CelebA dataset. The results suggest that private component can be removed in the cases where the the downstream task is known a priori (i.e., ``supervised''), and the case where it is not known a priori (i.e., ``weakly-supervised''). | Reject |
ICLR.cc/2020/Conference | HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled embedding of n-gram statistics | Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore, this paper tackles the domain of computationally efficient algorithms for NLP tasks. In particular, it investigates distributed representations of n-gram statistics of texts. The representations are formed using hyperdimensional computing enabled embedding. These representations then serve as features, which are used as input to standard classifiers. We investigate the applicability of the embedding on one large and three small standard datasets for classification tasks using nine classifiers. The embedding achieved on par F1 scores while decreasing the time and memory requirements by several times compared to the conventional n-gram statistics, e.g., for one of the classifiers on a small dataset, the memory reduction was 6.18 times; while train and test speed-ups were 4.62 and 3.84 times, respectively. For many classifiers on the large dataset, the memory reduction was about 100 times and train and test speed-ups were over 100 times. More importantly, the usage of distributed representations formed via hyperdimensional computing allows dissecting the strict dependency between the dimensionality of the representation and the parameters of n-gram statistics, thus, opening a room for tradeoffs. | Reject |
ICLR.cc/2019/Conference | Intriguing Properties of Learned Representations | A key feature of neural networks, particularly deep convolutional neural networks, is their ability to learn useful representations from data. The very last layer of a neural network is then simply a linear model trained on these learned representations. Despite their numerous applications in other tasks such as classification, retrieval, clustering etc., a.k.a. transfer learning, not much work has been published that investigates the structure of these representations or indeed whether structure can be imposed on them during the training process.
In this paper, we study the effective dimensionality of the learned representations by models that have proved highly successful for image classification. We focus on ResNet-18, ResNet-50 and VGG-19 and observe that when trained on CIFAR10 or CIFAR100, the learned representations exhibit a fairly low rank structure. We propose a modification to the training procedure, which further encourages low rank structure on learned activations. Empirically, we show that this has implications for robustness to adversarial examples and compression. | Reject |
ICLR.cc/2019/Conference | FEED: Feature-level Ensemble Effect for knowledge Distillation | This paper proposes a versatile and powerful training algorithm named Feature-level Ensemble Effect for knowledge Distillation(FEED), which is inspired by the work of factor transfer. The factor transfer is one of the knowledge transfer methods that improves the performance of a student network with a strong teacher network. It transfers the knowledge of a teacher in the feature map level using high-capacity teacher network, and our training algorithm FEED is an extension of it. FEED aims to transfer ensemble knowledge, using either multiple teachers in parallel or multiple training sequences. Adapting the peer-teaching framework, we introduce a couple of training algorithms that transfer ensemble knowledge to the student at the feature map level, both of which help the student network find more generalized solutions in the parameter space. Experimental results on CIFAR-100 and ImageNet show that our method, FEED, has clear performance enhancements,without introducing any additional parameters or computations at test time. | Reject |
ICLR.cc/2020/Conference | Semantic Hierarchy Emerges in the Deep Generative Representations for Scene Synthesis | Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed from random noises. In this work, we show that highly-structured semantic hierarchy emerges from the generative representations as the variation factors for synthesizing scenes. By probing the layer-wise representations with a broad set of visual concepts at different abstraction levels, we are able to quantify the causality between the activations and the semantics occurring in the output image. Such a quantification identifies the human-understandable variation factors learned by GANs to compose scenes. The qualitative and quantitative results suggest that the generative representations learned by GAN are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme. Identifying such a set of manipulatable latent semantics facilitates semantic scene manipulation. | Reject |
ICLR.cc/2020/Conference | Graph Convolutional Reinforcement Learning | Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, where agents keep moving and their neighbors change quickly. This makes it hard to learn abstract representations of mutual interplay between agents. To tackle these difficulties, we propose graph convolutional reinforcement learning, where graph convolution adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations. Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and cooperation is further improved by temporal relation regularization for consistency. Empirically, we show that our method substantially outperforms existing methods in a variety of cooperative scenarios. | Accept (Poster) |
ICLR.cc/2021/Conference | Out-of-Core Training for Extremely Large-Scale Neural Networks with Adaptive Window-Based Scheduling | While large neural networks demonstrate higher performance in various tasks, training large networks is difficult due to limitations on GPU memory size.
We propose a novel out-of-core algorithm that enables faster training of extremely large-scale neural networks with sizes larger than allotted GPU memory.
Under a given memory budget constraint, our scheduling algorithm locally adapts the timing of memory transfers according to memory usage of each function, which improves overlap between computation and memory transfers.
Additionally, we apply virtual addressing technique, commonly performed in OS, to training of neural networks with out-of-core execution, which drastically reduces the amount of memory fragmentation caused by frequent memory transfers. With our proposed algorithm, we successfully train ResNet-50 with 1440 batch-size with keeping training speed at 55\%, which is 7.5x larger than the upper bound of physical memory. It also outperforms a previous state-of-the-art substantially, i.e. it trains a 1.55x larger network than state-of-the-art with faster execution. Moreover, we experimentally show that our approach is also scalable for various types of networks. | Withdrawn |
ICLR.cc/2021/Conference | How Important is the Train-Validation Split in Meta-Learning? | Meta-learning aims to perform fast adaptation on a new task through learning a "prior" from multiple existing tasks. A common practice in meta-learning is to perform a train-validation split where the prior adapts to the task on one split of the data, and the resulting predictor is evaluated on another split. Despite its prevalence, the importance of the train-validation split is not well understood either in theory or in practice, particularly in comparison to the more direct non-splitting method, which uses all the per-task data for both training and evaluation.
We provide a detailed theoretical study on the whether and when the train-validation split is helpful on the linear centroid meta-learning problem, in the asymptotic setting where the number of tasks goes to infinity. We show that the splitting method converges to the optimal prior as expected, whereas the non-splitting method does not in general without structural assumptions on the data. In contrast, if the data are generated from linear models (the realizable regime), we show that both the splitting and non-splitting methods converge to the optimal prior. Further, perhaps surprisingly, our main result shows that the non-splitting method achieves a strictly better asymptotic excess risk under this data distribution, even when the regularization parameter and split ratio are optimally tuned for both methods. Our results highlight that data splitting may not always be preferable, especially when the data is realizable by the model. We validate our theories by experimentally showing that the non-splitting method can indeed outperform the splitting method, on both simulations and real meta-learning tasks. | Reject |
ICLR.cc/2022/Conference | Teacher's pet: understanding and mitigating biases in distillation | Knowledge distillation is widely used as a means of improving the performance of a relatively simple “student” model using the predictions from a complex “teacher” model. Several works have shown that distillation significantly boosts the student’s overall performance; however, are these gains uniform across all data sub-groups? In this paper, we show that distillation can harm performance on certain subgroups, e.g., classes with few associated samples, compared to the vanilla student trained using the one-hot labels. We trace this behavior to errors made by the teacher distribution being transferred to and amplified by the student model. To mitigate this problem, we present techniques which soften the teacher influence for subgroups where it is less reliable. Experiments on several image classification benchmarks show that these modifications of distillation maintain boost in overall accuracy, while additionally ensuring improvement in subgroup performance. | Reject |
ICLR.cc/2023/Conference | What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? | When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism.
Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance. We consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC as well as coverage for selective accuracy constraint.
We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 523 existing pretrained deep ImageNet classifiers that are available in popular repositories.
We identify numerous and previously unknown factors that affect uncertainty estimation and examine the relationships between the different metrics. We find that distillation-based training regimes consistently yield better uncertainty estimations than other training schemes such as vanilla training, pretraining on a larger dataset and adversarial training.
Moreover, we find a subset of ViT models that outperform any other models in terms of uncertainty estimation performance.
For example, we discovered an unprecedented 99% top-1 selective accuracy on ImageNet at 47% coverage
(and 95% top-1 accuracy at 80%) for a ViT model, whereas a competing EfficientNet-V2-XL cannot obtain these accuracy constraints at any level of coverage.
Our companion paper, also published in ICLR 2023 (A framework for benchmarking class-out-of-distribution detection and its application to ImageNet), examines the performance of these classifiers in a class-out-of-distribution setting. | Accept: poster |
ICLR.cc/2020/Conference | Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee | Over-parameterized deep neural networks trained by simple first-order methods are known to be able to fit any labeling of data. Such over-fitting ability hinders generalization when mislabeled training examples are present. On the other hand, simple regularization methods like early-stopping can often achieve highly nontrivial performance on clean test data in these scenarios, a phenomenon not theoretically understood. This paper proposes and analyzes two simple and intuitive regularization methods: (i) regularization by the distance between the network parameters to initialization, and (ii) adding a trainable auxiliary variable to the network output for each training example. Theoretically, we prove that gradient descent training with either of these two methods leads to a generalization guarantee on the clean data distribution despite being trained using noisy labels. Our generalization analysis relies on the connection between wide neural network and neural tangent kernel (NTK). The generalization bound is independent of the network size, and is comparable to the bound one can get when there is no label noise. Experimental results verify the effectiveness of these methods on noisily labeled datasets. | Accept (Poster) |
ICLR.cc/2023/Conference | Learning to Generate Columns with Application to Vertex Coloring | We present a new column generation approach based on Machine Learning (ML) for solving combinatorial optimization problems. The aim of our method is to generate high-quality columns that belong to an optimal integer solution, in contrast to the traditional approach that aims at solving linear programming relaxations. To achieve this aim, we design novel features to characterize a column, and develop an effective ML model to predict whether a column belongs to an optimal integer solution. We then use the ML model as a filter to select high-quality columns generated from a sampling method and use the selected columns to construct an integer solution. Our method is computationally fast compared to the traditional methods that generate columns by repeatedly solving a pricing problem. We demonstrate the efficacy of our method on the vertex coloring problem, by empirically showing that the columns selected by our ML model are significantly better, in terms of the integer solution that can be constructed from them, than those selected randomly or based only on their reduced cost. Further, we show that the columns generated by our method can be used as a warm start to boost the performance of a column generation-based heuristic. | Accept: poster |
ICLR.cc/2020/Conference | Sparse Networks from Scratch: Faster Training without Losing Performance | We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training. In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network. | Reject |
ICLR.cc/2019/Conference | h-detach: Modifying the LSTM Gradient Towards Better Optimization | Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately over a large number of steps. We introduce a simple stochastic algorithm (\textit{h}-detach) that is specific to LSTM optimization and targeted towards addressing this problem. Specifically, we show that when the LSTM weights are large, the gradient components through the linear path (cell state) in the LSTM computational graph get suppressed. Based on the hypothesis that these components carry information about long term dependencies (which we show empirically), their suppression can prevent LSTMs from capturing them. Our algorithm\footnote{Our code is available at https://github.com/bhargav104/h-detach.} prevents gradients flowing through this path from getting suppressed, thus allowing the LSTM to capture such dependencies better. We show significant improvements over vanilla LSTM gradient based training in terms of convergence speed, robustness to seed and learning rate, and generalization using our modification of LSTM gradient on various benchmark datasets. | Accept (Poster) |
ICLR.cc/2021/Conference | Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning | Sampling-based neural architecture search (NAS) always guarantees better convergence yet suffers from huge computational resources compared with gradient-based approaches, due to the rollout bottleneck -- exhaustive training for each sampled generation on proxy tasks. This work provides a general pipeline to accelerate the convergence of the rollout process as well as the RL learning process in sampling-based NAS. It is motivated by the interesting observation that both the architecture and the parameter knowledge can be transferred between different experiments and even different tasks. We first introduce an uncertainty-aware critic (value function) in PPO to utilize the architecture knowledge in previous experiments, which stabilizes the training process and reduces the searching time by 4 times. Further, a life-long knowledge pool together with a block similarity function is proposed to utilize the lifelong parameter knowledge and reduces the searching time by 2 times. It is the first to introduce block-level weight sharing in RL-based NAS. The block similarity function guarantees a 100% hitting ratio with strict fairness. Besides, we show a simply designed off-policy correction factor that enables 'replay buffer' in RL optimization and further reduces half of the searching time. Experiments on the MNAS search space show the proposed FNAS accelerates standard RL-based NAS process by $\sim$10x (e.g. $\sim$256 2x2 TPUv2*days / 20,000 GPU*hour $\rightarrow$ 2,000 GPU*hour for MNAS), and guarantees better performance on various vision tasks. | Reject |
ICLR.cc/2023/Conference | Cold Posteriors through PAC-Bayes | We investigate the cold posterior effect through the lens of PAC-Bayes generalization bounds. We argue that in the non-asymptotic setting, when the number of training samples is (relatively) small, discussions of the cold posterior effect should take into account that approximate Bayesian inference does not readily provide guarantees of performance on out-of-sample data. Instead, out-of-sample error is better described through a generalization bound. In this context, we explore the connections of the ELBO objective from variational inference and the PAC-Bayes objectives. We note that, while the ELBO and PAC-Bayes objectives are similar, the latter objectives naturally contain a temperature parameter $\lambda$ which is not restricted to be $\lambda=1$. For both simplified regression and realistic classification tasks, in the case of Laplace approximations to the posterior, we show how this PAC-Bayesian interpretation of the temperature parameter captures important aspects of the cold posterior effect. | Reject |
ICLR.cc/2019/Conference | Label super-resolution networks | We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data.
We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models. | Accept (Poster) |
ICLR.cc/2022/Conference | Finetuned Language Models are Zero-Shot Learners | This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning—finetuning language models on a collection of datasets described via instructions—substantially improves zero-shot performance on unseen tasks. We take a 137B parameter pretrained language model and instruction tune it on over 60 NLP datasets verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 datasets that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze. Ablation studies reveal that number of finetuning datasets, model scale, and natural language instructions are key to the success of instruction tuning. | Accept (Oral) |
ICLR.cc/2021/Conference | Computing Preimages of Deep Neural Networks with Applications to Safety | To apply an algorithm in a sensitive domain it is important to understand the set of input values that result in specific decisions. Deep neural networks suffer from an inherent instability that makes this difficult: different outputs can arise from very similar inputs.
We present a method to check that the decisions of a deep neural network are as intended by constructing the exact, analytical preimage of its predictions. Preimages generalize verification in the sense that they can be used to verify a wide class of properties, and answer much richer questions besides. We examine the functioning and failures of neural networks used in robotics, including an aircraft collision avoidance system, related to sequential decision making and extrapolation.
Our method iterates backwards through the layers of piecewise linear deep neural networks. Uniquely, we compute \emph{all} intermediate values that correspond to a prediction, propagating this calculation through layers using analytical formulae for layer preimages.
| Reject |
ICLR.cc/2018/Conference | Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference | The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on $K$ samples we show the bias to be of order O(1/K). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI),
a family of bias-reduced estimators reducing the bias to $O(K^{-(m+1)})$ for any given m < K while retaining computational efficiency. Finally, we demonstrate that JVI leads to improved evidence estimates in variational autoencoders. We also report first results on applying JVI to learning variational autoencoders.
Our implementation is available at https://github.com/Microsoft/jackknife-variational-inference | Accept (Poster) |
ICLR.cc/2019/Conference | Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics | Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learning, state representation learning can help learn a compact, efficient and relevant representation of states that speeds up policy learning, reducing the number of samples needed, and that is easier to interpret. We evaluate several state representation learning methods on goal based robotics tasks and propose a new unsupervised model that stacks representations and combines strengths of several of these approaches. This method encodes all the relevant features, performs on par or better than end-to-end learning, and is robust to hyper-parameters change. | Reject |
ICLR.cc/2022/Conference | Modeling and Eliminating Adversarial Examples using Function Theory of Several Complex Variables | The reliability of a learning model is key to the successful deployment of machine learning in various industries. Training a robust model, unaffected by adversarial attacks, requires a comprehensive understanding of the adversarial examples phenomenon. This paper presents a model and a solution for the existence and transfer of adversarial examples in analytic hypotheses. Grounded in the function theory of several complex variables, we propose the class of complex-valued holomorphic hypotheses as a natural way to represent the submanifold of the samples and the decision boundary simultaneously. To describe the mechanism in which the adversarial examples occur and transfer, we specialize the definitions of the optimal Bayes and the maximum margin classifiers to this class of hypotheses. The approach is validated initially on both synthetic and real-world classification problems using polynomials. Backed by theoretical and experimental results, we believe the analysis to apply to other classes of analytic hypotheses such as neural networks. | Withdrawn |
ICLR.cc/2020/Conference | Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers | We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation. We demonstrate that our dynamic sparse training algorithm can easily train very sparse neural network models with little performance loss using the same training epochs as dense models. Dynamic Sparse Training achieves prior art performance compared with other sparse training algorithms on various network architectures. Additionally, we have several surprising observations that provide strong evidence to the effectiveness and efficiency of our algorithm. These observations reveal the underlying problems of traditional three-stage pruning algorithms and present the potential guidance provided by our algorithm to the design of more compact network architectures. | Accept (Poster) |
ICLR.cc/2019/Conference | Provable Defenses against Spatially Transformed Adversarial Inputs: Impossibility and Possibility Results | One intriguing property of neural networks is their inherent vulnerability to adversarial inputs, which are maliciously crafted samples to trigger target networks to misbehave. The state-of-the-art attacks generate adversarial inputs using either pixel perturbation or spatial transformation. Thus far, several provable defenses have been proposed against pixel perturbation-based attacks; yet, little is known about whether such solutions exist for spatial transformation-based attacks. This paper bridges this striking gap by conducting the first systematic study on provable defenses against spatially transformed adversarial inputs. Our findings convey mixed messages. On the impossibility side, we show that such defenses may not exist in practice: for any given networks, it is possible to find legitimate inputs and imperceptible transformations to generate adversarial inputs that force arbitrarily large errors. On the possibility side, we show that it is still feasible to construct adversarial training methods to significantly improve the resilience of networks against adversarial inputs over empirical datasets. We believe our findings provide insights for designing more effective defenses against spatially transformed adversarial inputs. | Reject |
ICLR.cc/2020/Conference | Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks | The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affecting both convergence times and model performance. Yet despite significant empirical and theoretical analysis, relatively little has been proved about the concrete effects of different initialization schemes. In this work, we analyze the effect of initialization in deep linear networks, and provide for the first time a rigorous proof that drawing the initial weights from the orthogonal group speeds up convergence relative to the standard Gaussian initialization with iid weights. We show that for deep networks, the width needed for efficient convergence to a global minimum with orthogonal initializations is independent of the depth, whereas the width needed for efficient convergence with Gaussian initializations scales linearly in the depth. Our results demonstrate how the benefits of a good initialization can persist throughout learning, suggesting an explanation for the recent empirical successes found by initializing very deep non-linear networks according to the principle of dynamical isometry. | Accept (Poster) |
ICLR.cc/2019/Conference | Learning to Reinforcement Learn by Imitation | Meta-reinforcement learning aims to learn fast reinforcement learning (RL) procedures that can be applied to new tasks or environments. While learning fast RL procedures holds promise for allowing agents to autonomously learn a diverse range of skills, existing methods for learning efficient RL are impractical for real world settings, as they rely on slow reinforcement learning algorithms for meta-training, even when the learned procedures are fast. In this paper, we propose to learn a fast reinforcement learning procedure through supervised imitation of an expert, such that, after meta-learning, an agent can quickly learn new tasks through trial-and-error. Through our proposed method, we show that it is possible to learn fast RL using demonstrations, rather than relying on slow RL, where expert agents can be trained quickly by using privileged information or off-policy RL methods. Our experimental evaluation on a number of complex simulated robotic domains demonstrates that our method can effectively learn to learn from spare rewards and is significantly more efficient than prior meta reinforcement learning algorithms. | Reject |
ICLR.cc/2022/Conference | Discovering Invariant Rationales for Graph Neural Networks | Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features --- rationale --- which guides the model prediction. Unfortunately, the leading rationalization models often rely on data biases, especially shortcut features, to compose rationales and make predictions without probing the critical and causal patterns. Moreover, such data biases easily change outside the training distribution. As a result, these models suffer from a huge drop in interpretability and predictive performance on out-of-distribution data. In this work, we propose a new strategy of discovering invariant rationale (DIR) to construct intrinsically interpretable GNNs. It conducts interventions on the training distribution to create multiple interventional distributions. Then it approaches the causal rationales that are invariant across different distributions while filtering out the spurious patterns that are unstable. Experiments on both synthetic and real-world datasets validate the superiority of our DIR in terms of interpretability and generalization ability on graph classification over the leading baselines. Code and datasets are available at https://github.com/Wuyxin/DIR-GNN. | Accept (Poster) |
ICLR.cc/2023/Conference | Neural Episodic Control with State Abstraction | Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency. Generally, episodic control-based approaches are solutions that leverage highly rewarded past experiences to improve sample efficiency of DRL algorithms. However, previous episodic control-based approaches fail to utilize the latent information from the historical behaviors (\eg, state transitions, topological similarities, \etc) and lack scalability during DRL training. This work introduces Neural Episodic Control with State Abstraction (NECSA), a simple but effective state abstraction-based episodic control containing a more comprehensive episodic memory, a novel state evaluation, and a multi-step state analysis. We evaluate our approach to the MuJoCo and Atari tasks in OpenAI gym domains. The experimental results indicate that NECSA achieves higher sample efficiency than the state-of-the-art episodic control-based approaches. Our data and code are available at the project website\footnote{\url{https://sites.google.com/view/drl-necsa}}. | Accept: notable-top-25% |
ICLR.cc/2022/Conference | Efficient Image Representation Learning with Federated Sampled Softmax | Learning image representations on decentralized data can bring many benefits in cases where data cannot be aggregated across data silos. Softmax cross entropy loss is highly effective and commonly used for learning image representations. Using a large number of classes has proven to be particularly beneficial for the descriptive power of such representations in centralized learning. However, doing so on decentralized data with Federated Learning is not straightforward, as the demand on computation and communication increases proportionally to the number of classes. In this work we introduce Federated Sampled Softmax, a novel resource-efficient approach for learning image representation with Federated Learning. Specifically, the FL clients sample a set of negative classes and optimize only the corresponding model parameters with respect to a sampled softmax objective that approximates the global full softmax objective. We analytically examine the loss formulation and empirically show that our method significantly reduces the number of parameters transferred to and optimized by the client devices, while performing on par with the standard full softmax method. This work creates a possibility for efficiently learning image representations on decentralized data with a large number of classes in a privacy preserving way. | Reject |
ICLR.cc/2018/Conference | Fast and Accurate Inference with Adaptive Ensemble Prediction for Deep Networks | Ensembling multiple predictions is a widely-used technique to improve the accuracy of various machine learning tasks. In image classification tasks, for example, averaging the predictions for multiple patches extracted from the input image significantly improves accuracy. Using multiple networks trained independently to make predictions improves accuracy further. One obvious drawback of the ensembling technique is its higher execution cost during inference.% If we average 100 local predictions, the execution cost will be 100 times as high as the cost without the ensemble. This higher cost limits the real-world use of ensembling. In this paper, we first describe our insights on relationship between the probability of the prediction and the effect of ensembling with current deep neural networks; ensembling does not help mispredictions for inputs predicted with a high probability, i.e. the output from the softmax. This finding motivates us to develop a new technique called adaptive ensemble prediction, which achieves the benefits of ensembling with much smaller additional execution costs. Hence, we calculate the confidence level of the prediction for each input from the probabilities of the local predictions during the ensembling computation. If the prediction for an input reaches a high enough probability on the basis of the confidence level, we stop ensembling for this input to avoid wasting computation power. We evaluated the adaptive ensembling by using various datasets and showed that it reduces the computation cost significantly while achieving similar accuracy to the naive ensembling. We also showed that our statistically rigorous confidence-level-based termination condition reduces the burden of the task-dependent parameter tuning compared to the naive termination based on the pre-defined threshold in addition to yielding a better accuracy with the same cost.
| Reject |
ICLR.cc/2022/Conference | Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective | Deep ensemble learning has been shown to improve accuracy by training multiple neural networks and fusing their outputs. Ensemble learning has also been used to defend against membership inference attacks that undermine privacy. In this paper, we empirically demonstrate a trade-off between these two goals, namely accuracy and privacy (in terms of membership inference attacks), in deep ensembles. Using a wide range of datasets and model architectures, we show that the effectiveness of membership inference attacks also increases when ensembling improves accuracy. To better understand this trade-off, we study the impact of various factors such as prediction confidence and agreement between models that constitute the ensemble. Finally, we evaluate defenses against membership inference attacks based on regularization and differential privacy. We show that while these defenses can mitigate the effectiveness of the membership inference attack, they simultaneously degrade ensemble accuracy. We illustrate similar trade-off in more advanced and state-of-the-art ensembling techniques, such as snapshot ensembles and diversified ensemble networks. The source code is available in supplementary materials. | Reject |
ICLR.cc/2019/Conference | Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching | We consider the problem of training speech recognition systems without using any labeled data, under the assumption that the learner can only access to the input utterances and a phoneme language model estimated from a non-overlapping corpus. We propose a fully unsupervised learning algorithm that alternates between solving two sub-problems: (i) learn a phoneme classifier for a given set of phoneme segmentation boundaries, and (ii) refining the phoneme boundaries based on a given classifier. To solve the first sub-problem, we introduce a novel unsupervised cost function named Segmental Empirical Output Distribution Matching, which generalizes the work in (Liu et al., 2017) to segmental structures. For the second sub-problem, we develop an approximate MAP approach to refining the boundaries obtained from Wang et al. (2017). Experimental results on TIMIT dataset demonstrate the success of this fully unsupervised phoneme recognition system, which achieves a phone error rate (PER) of 41.6%. Although it is still far away from the state-of-the-art supervised systems, we show that with oracle boundaries and matching language model, the PER could be improved to 32.5%. This performance approaches the supervised system of the same model architecture, demonstrating the great potential of the proposed method. | Accept (Poster) |
ICLR.cc/2022/Conference | Few-Shot Multi-task Learning via Implicit regularization | Modern machine learning is highly data-intensive. Few-shot learning (FSL) aims to resolve this sample efficiency problem by learning from multiple tasks and quickly adapt to new tasks containing only a few samples. However, FSL problems proves to be significantly more challenging and require more compute expensive process to optimize. In this work, we consider multi-task linear regression (MTLR) as a canonical problem for few-shot learning, and investigate the source of challenge of FSL. We find that the MTLR exhibits local minimum problems that are not present in single-task problem, and thus making the learning much more challenging. We also show that the problem can be resolved by overparameterizing the model by increasing both the width and depth of the linear network and initializing the weights with small values, exploiting the implicit regularization bias of gradient descent-based learning. | Withdrawn |
ICLR.cc/2022/Conference | Multimeasurement Generative Models | We formally map the problem of sampling from an unknown distribution with a density in $\mathbb{R}^d$ to the problem of learning and sampling a smoother density in $\mathbb{R}^{Md}$ obtained by convolution with a fixed factorial kernel: the new density is referred to as M-density and the kernel as multimeasurement noise model (MNM). The M-density in $\mathbb{R}^{Md}$ is smoother than the original density in $\mathbb{R}^d$, easier to learn and sample from, yet for large $M$ the two problems are mathematically equivalent since clean data can be estimated exactly given a multimeasurement noisy observation using the Bayes estimator. To formulate the problem, we derive the Bayes estimator for Poisson and Gaussian MNMs in closed form in terms of the unnormalized M-density. This leads to a simple least-squares objective for learning parametric energy and score functions. We present various parametrization schemes of interest including one in which studying Gaussian M-densities directly leads to multidenoising autoencoders—this is the first theoretical connection made between denoising autoencoders and empirical Bayes in the literature. Samples in $\mathbb{R}^d$ are obtained by walk-jump sampling (Saremi & Hyvarinen, 2019) via underdamped Langevin MCMC (walk) to sample from M-density and the multimeasurement Bayes estimation (jump). We study permutation invariant Gaussian M-densities on MNIST, CIFAR-10, and FFHQ-256 datasets, and demonstrate the effectiveness of this framework for realizing fast-mixing stable Markov chains in high dimensions. | Accept (Poster) |
ICLR.cc/2023/Conference | Private and Efficient Meta-Learning with Low Rank and Sparse decomposition | Meta-learning is critical for a variety of practical ML systems -- like personalized recommendations systems -- that are required to generalize to new tasks despite a small number of task-specific training points. Existing meta-learning techniques use two complementary approaches of either learning a low-dimensional representation of points for all tasks, or task-specific fine-tuning of a global model trained using all the tasks. In this work, we propose a novel meta-learning framework that combines both the techniques to enable handling of a large number of data-starved tasks. Our framework models network weights as a sum of low-rank and sparse matrices. This allows us to capture information from multiple domains together in the low-rank part while still allowing task specific personalization using the sparse part. We instantiate and study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements. We propose an alternating minimization method with hard thresholding -- AMHT-LRS -- to learn the low-rank and sparse part effectively and efficiently. For the realizable, Gaussian data setting, we show that AMHT-LRS indeed solves the problem efficiently with nearly optimal samples. We extend AMHT-LRS to ensure that it preserves privacy of each individual user in the dataset, while still ensuring strong generalization with nearly optimal number of samples. Finally, on multiple datasets, we demonstrate that the framework allows personalized models to obtain superior performance in the data-scarce regime. | Reject |
ICLR.cc/2021/Conference | Neural Delay Differential Equations | Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework. Here we propose a new class of continuous-depth neural networks with delay, named as Neural Delay Differential Equations (NDDEs), and, for computing the corresponding gradients, we use the adjoint sensitivity method to obtain the delayed dynamics of the adjoint. Since the differential equations with delays are usually seen as dynamical systems of infinite dimension possessing more fruitful dynamics, the NDDEs, compared to the NODEs, own a stronger capacity of nonlinear representations. Indeed, we analytically validate that the NDDEs are of universal approximators, and further articulate an extension of the NDDEs, where the initial function of the NDDEs is supposed to satisfy ODEs. More importantly, we use several illustrative examples to demonstrate the outstanding capacities of the NDDEs and the NDDEs with ODEs' initial value. More precisely, (1) we successfully model the delayed dynamics where the trajectories in the lower-dimensional phase space could be mutually intersected, while the traditional NODEs without any argumentation are not directly applicable for such modeling, and (2) we achieve lower loss and higher accuracy not only for the data produced synthetically by complex models but also for the real-world image datasets, i.e., CIFAR10, MNIST and SVHN. Our results on the NDDEs reveal that appropriately articulating the elements of dynamical systems into the network design is truly beneficial to promoting the network performance. | Accept (Poster) |
ICLR.cc/2023/Conference | GANet: Graph-Aware Network for Point Cloud Completion with Displacement-Aware Point Augmentor | Remarkably, real-world data (e.g., LiDAR-based point clouds) is commonly sparse, uneven, occluded, and truncated. The point cloud completion task draws due attention, which aims to predict a complete and accurate shape from its partial observation. However, existing methods commonly adopt PointNet or PointNet++ to extract features of incomplete point clouds. In this paper, we propose an end-to-end Graph-Aware Network (\textbf{GANet}) to effectively learn from the contour information of the partial point clouds. Moreover, we design Displacements-Aware Augmentor (DPA) to upsample and refine coarse point clouds. With our graph-based feature extractors and Displacements-Aware Transformer, our DPA can precisely capture the geometric and structural features to refine the complete point clouds. Experiments on PCN and MVP datasets demonstrate that our GANet achieves state-of-the-art on the task of shape completion. | Reject |
ICLR.cc/2023/Conference | StepGCN: Step-oriented Graph Convolutional Networks in Representation Learning | Graph Convolutional Networks (GCNs) are employed to address a number of tasks in our society with their representation learning approach. Nonetheless, despite their effectiveness and usefulness, the majority of GCN-oriented approaches have an over-smoothing concern. Over-smoothing is the problem of node representations converging into a certain value, making the nodes indistinguishable. To effectively address the over-smoothing problem, we introduce StepGCN, a GCN model that integrates step learning techniques with graph residual connection networks. With our StepGCN, we achieved significant performance improvements in multiple representation learning benchmark datasets, and demonstrate that step learning can be expanded to other graph networks. | Withdrawn |
ICLR.cc/2022/Conference | C5T5: Controllable Generation of Organic Molecules with Transformers | Methods for designing organic materials with desired properties have high potential impact across fields such as medicine, renewable energy, petrochemical engineering, and agriculture. However, using generative models for this task is difficult because candidate compounds must satisfy many constraints, including synthetic accessibility, intellectual property attributes, ``chemical beauty'' (Bickerton et al., 2020), and other considerations that are intuitive to domain experts but can be challenging to quantify. We propose C5T5, a novel self-supervised pretraining method that works in tandem with domain experts by making zero-shot select-and-replace edits, altering organic substances towards desired property values. C5T5 operates on IUPAC names---a standardized molecular representation that intuitively encodes rich structural information for organic chemists but that has been largely ignored by the ML community. Our technique requires no edited molecule pairs to train and only a rough estimate of molecular properties, and it has the potential to model long-range dependencies and symmetric molecular structures more easily than graph-based methods. We demonstrate C5T5's effectiveness on four physical properties relevant for drug discovery, showing that it learns successful and chemically intuitive strategies for altering molecules towards desired property values.
| Reject |
ICLR.cc/2023/Conference | Bridging the Gap to Real-World Object-Centric Learning | Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real world-datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature. | Accept: poster |
ICLR.cc/2021/Conference | Meta Gradient Boosting Neural Networks | Meta-optimization is an effective approach that learns a shared set of parameters across tasks for parameter initialization in meta-learning.
A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.
To address this issue, we design a meta-gradient boosting framework that uses a base learner to learn shared information across tasks and a series of gradient-boosted modules to capture task-specific information to fit diverse distributions.
We evaluate the proposed model on both regression and classification tasks with multi-mode distributions.
The results demonstrate both the effectiveness of our model in modulating task-specific meta-learned priors and its advantages on multi-mode distributions. | Reject |
ICLR.cc/2021/Conference | Joint Perception and Control as Inference with an Object-based Implementation | Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a general framework to combine perception and control for partially observable environments through Bayesian inference. Based on the fact that object-level inductive biases are critical in human perceptual learning and reasoning, we propose Object-based Perception Control (OPC), an instantiation of PCI which manages to facilitate control using automatic discovered object-based representations. We develop an unsupervised end-to-end solution and analyze the convergence of the perception model update. Experiments in a high-dimensional pixel environment demonstrate the learning effectiveness of our object-based perception control approach. Specifically, we show that OPC achieves good perceptual grouping quality and outperforms several strong baselines in accumulated rewards. | Reject |
ICLR.cc/2021/Conference | Towards Robust Graph Neural Networks against Label Noise | Massive labeled data have been used in training deep neural networks, thus label noise has become an important issue therein. Although learning with noisy labels has made great progress on image datasets in recent years, it has not yet been studied in connection with utilizing GNNs to classify graph nodes. In this paper, we proposed a method, named LPM, to address the problem using Label Propagation (LP) and Meta learning. Different from previous methods designed for image datasets, our method is based on a special attribute (label smoothness) of graph-structured data, i.e., neighboring nodes in a graph tend to have the same label. A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label. Experimental results demonstrate that LPM outperforms state-of-the-art methods in graph node classification task with both synthetic and real-world label noise. Source code to reproduce all results will be released. | Reject |
ICLR.cc/2020/Conference | GENERALIZATION GUARANTEES FOR NEURAL NETS VIA HARNESSING THE LOW-RANKNESS OF JACOBIAN | Modern neural network architectures often generalize well despite containing many more parameters than the size of the training dataset. This paper explores the generalization capabilities of neural networks trained via gradient descent. We develop a data-dependent optimization and generalization theory which leverages the low-rank structure of the Jacobian matrix associated with the network. Our results help demystify why training and generalization is easier on clean and structured datasets and harder on noisy and unstructured datasets as well as how the network size affects the evolution of the train and test errors during training. Specifically, we use a control knob to split the Jacobian spectum into ``information" and ``nuisance" spaces associated with the large and small singular values. We show that over the information space learning is fast and one can quickly train a model with zero training loss that can also generalize well. Over the nuisance space training is slower and early stopping can help with generalization at the expense of some bias. We also show that the overall generalization capability of the network is controlled by how well the labels are aligned with the information space. A key feature of our results is that even constant width neural nets can provably generalize for sufficiently nice datasets. We conduct various numerical experiments on deep networks that corroborate our theoretical findings and demonstrate that: (i) the Jacobian of typical neural networks exhibit low-rank structure with a few large singular values and many small ones leading to a low-dimensional information space, (ii) over the information space learning is fast and most of the labels falls on this space, and (iii) label noise falls on the nuisance space and impedes optimization/generalization. | Reject |
ICLR.cc/2023/Conference | HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention | The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding. The concise design brings CLIP the advantage in inference efficiency against other vision-language models with heavier cross-attention fusion layers, making it a popular choice for a wide spectrum of downstream tasks. However, CLIP does not explicitly capture the hierarchical nature of high-level and fine-grained semantics conveyed in images and texts, which is arguably critical to vision-language understanding and reasoning. To this end, we equip both the visual and language branches in CLIP with hierarchy-aware attentions, namely Hierarchy-aware CLIP (HiCLIP), to progressively discover semantic hierarchies layer-by-layer from both images and texts in an unsupervised manner. As a result, such hierarchical aggregation significantly improves the cross-modal alignment. To demonstrate the advantages of HiCLIP, we conduct qualitative analysis on its unsupervised hierarchy induction during inference, as well as extensive quantitative experiments on both visual recognition and vision-language downstream tasks. | Accept: poster |
ICLR.cc/2023/Conference | Efficient Proxy for NAS is Extensible Now | Neural Architecture Search (NAS) has become a de facto approach in the recent trend of AutoML to design deep neural networks (DNNs). Efficient or near-zero-cost NAS proxies are further proposed to address the demanding computational issues of NAS, where each candidate architecture network only requires one iteration of backpropagation. The values obtained from the proxies are considered the predictions of architecture performance on downstream tasks. However, two significant drawbacks hinder the extended usage of Efficient NAS proxies. (1) Efficient proxies are not adaptive to various search spaces. (2) Efficient proxies are not extensible to multi-modality downstream tasks. Based on the observations, we design a Extensible proxy (Eproxy) that utilizes self-supervised, few-shot training (i.e., 10 iterations of backpropagation) which yields near-zero costs. The key component that makes Eproxy efficient is an untrainable convolution layer termed barrier layer that add the non-linearities to the optimization spaces so that the Eproxy can discriminate the performance of architectures in the early stage. Furthermore, to make Eproxy adaptive to different downstream tasks/search spaces, we propose a Discrete Proxy Search (DPS) to find the optimized training settings for Eproxy with only handful of benchmarked architectures on the target tasks. Our extensive experiments confirm the effectiveness of both Eproxy and Eproxy+DPS. On NAS-Bench-101 (~423k architectures), Eproxy achieves 0.65 as the spearman rho. In contrast, the previous best zero-cost method achieves 0.45. On NDS-ImageNet search spaces, Eproxy+DPS delivers 0.73 Spearman $\rho$ average ranking correlation while the previous efficient proxy only achieves 0.47. On NAS-Bench-Trans-Micro search space (7 tasks), Eproxy+DPS delivers comparable performance with early stop methods which requires 660 GPU hours per task. For the end-to-end task such as DARTS-ImageNet-1k, our method delivers better results compared to NAS performed on CIFAR-10 while only requiring a GPU hour with a single batch of CIFAR-10 images. | Withdrawn |
ICLR.cc/2023/Conference | Building Normalizing Flows with Stochastic Interpolants | A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed. The velocity field of this flow is inferred from the probability current of a time-dependent density that interpolates between the base and the target in finite time. Unlike conventional normalizing flow inference methods based the maximum likelihood principle, which require costly backpropagation through ODE solvers, our interpolant approach leads to a simple quadratic loss for the velocity itself which is expressed in terms of expectations that are readily amenable to empirical estimation. The flow can be used to generate samples from either the base or target, and to estimate the likelihood at any time along the interpolant. In addition, the flow can be optimized to minimize the path length of the interpolant density, thereby paving the way for building optimal transport maps. In situations where the base is a Gaussian density, we also show that the velocity of our normalizing flow can also be used to construct a diffusion model to sample the target as well as estimate its score. However, our approach shows that we can bypass this diffusion completely and work at the level of the probability flow with greater simplicity, opening an avenue for methods based solely on ordinary differential equations as an alternative to those based on stochastic differential equations. Benchmarking on density estimation tasks illustrates that the learned flow can match and surpass conventional continuous flows at a fraction of the cost, and compares well with diffusions on image generation on CIFAR-10 and ImageNet $32 \times 32$. The method scales ab-initio ODE flows to previously unreachable image resolutions, demonstrated up to $128\times128$. | Accept: poster |
ICLR.cc/2023/Conference | A Mutual Information Duality Algorithm for Multi-Agent Specialization | The social behavior change in a population has long been studied as an essential component of multi-agent learning. The learning of behavioral change not only involves reinforcement learning (RL), but also be measured against the general population with mutual information (MI). The combination of RL and MI led us to derive MI optimizations from policy gradient. With MI as multi-agent's optimization objective, we discover that the dual properties of MI can result in distinctly different population behaviors. From MI maximization that maximizes the stability of a population to MI minimization that enables specialization among the agents, the dual of MI creates a significant change in a population's behavioral properties. In this paper, we propose a minimax formulation of MI (M\&M) that enables agents specialization with stable regularization. Empirically we evaluated M\&M against the prior SOTA MARL framework, and analyze the social behavior change in performance, diversity, and the stability of their social graphs. | Reject |
ICLR.cc/2023/Conference | On a Built-in Conflict between Deep Learning and Systematic Generalization | Out-of-distribution or systematic generalization is a desirable property that most deep learning algorithms lack. In this paper, we hypothesize that internal function sharing is one of the reasons to weaken systematic generalization in deep learning for classification tasks. Under equivalent prediction, a model partitions an input space into multiple parts separated by boundaries. The function sharing prefers to reuse boundaries, leading to fewer parts for new outputs, which conflicts with systematic generalization. We show such phenomena in standard deep learning models, such as fully connected, convolutional, residual networks, LSTMs, and (Vision) Transformers. We hope this study provides novel insights and forms a basis for new research directions to improve systematic generalization. | Reject |
ICLR.cc/2022/Conference | White Paper Assistance: A Step Forward Beyond the Shortcut Learning | The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested. We show through experiments that even over-parameterized models would still solve a dataset by recklessly leveraging spurious correlations, or so-called ``shortcuts’’. To combat with this unintended propensity, we borrow the idea of printer test page and propose a novel approach called White Paper Assistance. Our proposed method is two-fold; (a) we intentionally involves the white paper to detect the extent to which the model has preference for certain characterized patterns and (b) we debias the model by enforcing it to make a random guess on the white paper. We show the consistent accuracy improvements that are manifest in various architectures, datasets and combinations with other techniques. Experiments have also demonstrated the versatility of our approach on imbalanced classification and robustness to corruptions. | Reject |
ICLR.cc/2021/Conference | Quantile Regularization : Towards Implicit Calibration of Regression Models | Recent works have shown that most deep learning models are often poorly calibrated, i.e., they may produce overconfident
predictions that are wrong, implying that their uncertainty estimates are unreliable. While a number of approaches have been proposed recently to calibrate classification models, relatively little work exists on calibrating regression models. Isotonic Regression has recently been advocated for regression calibration. We provide a detailed formal analysis of the \emph{side-effects} of Isotonic Regression when used for regression calibration. To address this, we recast quantile calibration as entropy estimation, and leverage this idea to construct a novel quantile regularizer, which can be used in any optimization based probabilisitc regression models. Unlike most of the existing approaches for calibrating regression models, which are based on \emph{post-hoc} processing of the model's output, and require an additional dataset, our method is trainable in an end-to-end fashion, without requiring an additional dataset. We provide empirical results demonstrating that our approach improves calibration for regression models trained on diverse architectures that provide uncertainty estimates, such as Dropout VI, Deep Ensembles | Reject |
ICLR.cc/2019/Conference | Learning Programmatically Structured Representations with Perceptor Gradients | We present the perceptor gradients algorithm -- a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network extracting symbols from raw observation data and ii) a task encoding program which maps the input symbols to output actions. We show that the proposed algorithm is able to learn representations that can be directly fed into a Linear-Quadratic Regulator (LQR) or a general purpose A* planner. Our experimental results confirm that the perceptor gradients algorithm is able to efficiently learn transferable symbolic representations as well as generate new observations according to a semantically meaningful specification.
| Accept (Poster) |
ICLR.cc/2021/Conference | Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization | Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures, which are not restricted to being discrete. While past approaches rely on entropic or quadratic regularization, we employ input convex neural networks and cycle-consistency regularization to avoid introducing bias. As a result, our approach does not resort to minimax optimization. We provide theoretical analysis on error bounds as well as empirical evidence of the effectiveness of the proposed approach in low-dimensional qualitative scenarios and high-dimensional quantitative experiments. | Accept (Poster) |
ICLR.cc/2023/Conference | Finding the Global Semantic Representation in GAN through Fréchet Mean | The ideally disentangled latent space in GAN involves the global representation of latent space using semantic attribute coordinates. In other words, in this disentangled space, there exists the global semantic basis as a vector space where each basis component describes one attribute of generated images. In this paper, we propose an unsupervised method for finding this global semantic basis in the intermediate latent space in GANs. This semantic basis represents sample-independent meaningful perturbations that change the same semantic attribute of an image on the entire latent space. The proposed global basis, called Fréchet basis, is derived by introducing Fréchet mean to the local semantic perturbations in a latent space. Fréchet basis is discovered in two stages. First, the global semantic subspace is discovered by the Fréchet mean in the Grassmannian manifold of the local semantic subspaces. Second, Fréchet basis is found by optimizing a basis of the semantic subspace via the Fréchet mean in the Special Orthogonal Group. Experimental results demonstrate that Fréchet basis provides better semantic factorization and robustness compared to the previous methods. Moreover, we suggest the basis refinement scheme for the previous methods. The quantitative experiments show that the refined basis achieves better semantic factorization while constrained on the same semantic subspace given by the previous method. | Accept: poster |
ICLR.cc/2023/Conference | CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data | Mapping biological mechanisms in cellular systems is a fundamental step in early-stage drug discovery that serves to generate hypotheses on what disease-relevant molecular targets may effectively be modulated by pharmacological interventions. With the advent of high-throughput methods for measuring single-cell gene expression under genetic perturbations, we now have effective means for generating evidence for causal gene-gene interactions at scale. However, inferring graphical networks of the size typically encountered in real-world gene-gene interaction networks is difficult in terms of both achieving and evaluating faithfulness to the true underlying causal graph. Moreover, standardised benchmarks for comparing methods for causal discovery in perturbational single-cell data do not yet exist. Here, we introduce CausalBench - a comprehensive benchmark suite for evaluating network inference methods on large-scale perturbational single-cell gene expression data. CausalBench introduces several biologically meaningful performance metrics and operates on two large, curated and openly available benchmark data sets for evaluating methods on the inference of gene regulatory networks from single-cell data generated under perturbations. With real-world datasets consisting of over 200000 training samples under interventions, CausalBench could potentially help facilitate advances in causal network inference by providing what is - to the best of our knowledge - the largest openly available test bed for causal discovery from real-world perturbation data to date. | Reject |
ICLR.cc/2020/Conference | Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning | The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms. Their popularity stems from the intuitive interpretation of the maximum entropy objective and their superior sample efficiency on standard benchmarks. In this paper, we seek to understand the primary contribution of the entropy term to the performance of maximum entropy algorithms. For the Mujoco benchmark, we demonstrate that the entropy term in Soft Actor Critic (SAC) principally addresses the bounded nature of the action spaces. With this insight, we propose a simple normalization scheme which allows a streamlined algorithm without entropy maximization match the performance of SAC. Our experimental results demonstrate a need to revisit the benefits of entropy regularization in DRL. We also propose a simple non-uniform sampling method for selecting transitions from the replay buffer during training. We further show that the streamlined algorithm with the simple non-uniform sampling scheme outperforms SAC and achieves state-of-the-art performance on challenging continuous control tasks. | Reject |
ICLR.cc/2023/Conference | NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs | We aim to train accurate denoising networks for smartphone/digital cameras from single noisy images. Downscaling is commonly used as a practical denoiser for low-resolution images. Based on this processing, we found that the pixel variance of the natural images is more robust to downscaling than the pixel variance of the camera noises. Intuitively, downscaling easily removes high-frequency noises than natural textures. To utilize this property, we can adopt noisy/clean image synthesis at low-resolution to train camera denoisers. On this basis, we propose a new solution pipeline -- NERDS that estimates camera noises and synthesizes noisy-clean image pairs from only noisy images. In particular, it first models the noise in raw-sensor images as a Poisson-Gaussian distribution, then estimates the noise parameters using the difference of pixel variances by downscaling. We formulate the noise estimation as a gradient-descent-based optimization problem through a reparametrization trick. We further introduce a new Image Signal Processor (ISP) estimation method that enables denoiser training in a human-readable RGB space by transforming the synthetic raw images to the style of a given RGB noisy image. The noise and ISP estimations utilize rich augmentation to synthesize image pairs for denoiser training. Experiments show that our NERDS can accurately train CNN-based denoisers (e.g., DnCNN, ResNet-style network) outperforming previous noise-synthesis-based and self-supervision-based denoisers in real datasets. | Accept: poster |
ICLR.cc/2018/Conference | Graph Partition Neural Networks for Semi-Supervised Classification | We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we experiment with spectral partitioning and also propose a modified multi-seed flood fill for fast processing of large scale graphs. We extensively test our model on a variety of semi-supervised node classification tasks. Experimental results indicate that GPNNs are either superior or comparable to state-of-the-art methods on a wide variety of datasets for graph-based semi-supervised classification. We also show that GPNNs can achieve similar performance as standard GNNs with fewer propagation steps. | Invite to Workshop Track |
ICLR.cc/2020/Conference | AdaX: Adaptive Gradient Descent with Exponential Long Term Memory | Adaptive optimization algorithms such as RMSProp and Adam have fast convergence and smooth learning process. Despite their successes, they are proven to have non-convergence issue even in convex optimization problems as well as weak performance compared with the first order gradient methods such as stochastic gradient descent (SGD). Several other algorithms, for example AMSGrad and AdaShift, have been proposed to alleviate these issues but only minor effect has been observed. This paper further analyzes the performance of such algorithms in a non-convex setting by extending their non-convergence issue into a simple non-convex case and show that Adam's design of update steps would possibly lead the algorithm to local minimums. To address the above problems, we propose a novel adaptive gradient descent algorithm, named AdaX, which accumulates the long-term past gradient information exponentially. We prove the convergence of AdaX in both convex and non-convex settings. Extensive experiments show that AdaX outperforms Adam in various tasks of computer vision and natural language processing and can catch up with SGD.
| Reject |
ICLR.cc/2021/Conference | Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective | In the recent literature of Graph Neural Networks (GNN), the expressive power of models has been studied through their capability to distinguish if two given graphs are isomorphic or not. Since the graph isomorphism problem is NP-intermediate, and Weisfeiler-Lehman (WL) test can give sufficient but not enough evidence in polynomial time, the theoretical power of GNNs is usually evaluated by the equivalence of WL-test order, followed by an empirical analysis of the models on some reference inductive and transductive datasets. However, such analysis does not account the signal processing pipeline, whose capability is generally evaluated in the spectral domain. In this paper, we argue that a spectral analysis of GNNs behavior can provide a complementary point of view to go one step further in the understanding of GNNs. By bridging the gap between the spectral and spatial design of graph convolutions, we theoretically demonstrate some equivalence of the graph convolution process regardless it is designed in the spatial or the spectral domain. Using this connection, we managed to re-formulate most of the state-of-the-art graph neural networks into one common framework. This general framework allows to lead a spectral analysis of the most popular GNNs, explaining their performance and showing their limits according to spectral point of view. Our theoretical spectral analysis is confirmed by experiments on various graph databases. Furthermore, we demonstrate the necessity of high and/or band-pass filters on a graph dataset, while the majority of GNN is limited to only low-pass and inevitably it fails. | Accept (Poster) |
ICLR.cc/2018/Conference | Semi-Supervised Learning via New Deep Network Inversion | We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems.
The approach reaches current state-of-the-art methods on MNIST and provides reasonable performances on SVHN and CIFAR10. Through the introduced method, residual networks are for the first time applied to semi-supervised tasks. Experiments with one-dimensional signals highlight the generality of the method. Importantly, our approach is simple, efficient, and requires no change in the deep network architecture. | Reject |
ICLR.cc/2023/Conference | On the Convergence of AdaGrad(Norm) on $\mathbb{R}^d$: Beyond Convexity, Non-Asymptotic Rate and Acceleration | Existing analysis of AdaGrad and other adaptive methods for smooth convex optimization is typically for functions with bounded domain diameter. In unconstrained problems, previous works guarantee an asymptotic convergence rate without an explicit constant factor that holds true for the entire function class. Furthermore, in the stochastic setting, only a modified version of AdaGrad, different from the one commonly used in practice, in which the latest gradient is not used to update the stepsize, has been analyzed. Our paper aims at bridging these gaps and developing a deeper understanding of AdaGrad and its variants in the standard setting of smooth convex functions as well as the more general setting of quasar convex functions. First, we demonstrate new techniques to explicitly bound the convergence rate of the vanilla AdaGrad for unconstrained problems in both deterministic and stochastic settings. Second, we propose a variant of AdaGrad for which we can show the convergence of the last iterate, instead of the average iterate. Finally, we give new accelerated adaptive algorithms and their convergence guarantee in the deterministic setting with explicit dependency on the problem parameters, improving upon the asymptotic rate shown in previous works. | Accept: poster |
ICLR.cc/2021/Conference | Learning from deep model via exploring local targets | Deep neural networks often have huge number of parameters, which posts challenges in deployment in application scenarios with limited memory and computation capacity. Knowledge distillation is one approach to derive compact models from bigger ones.
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima. In this paper, we propose proKT, a new model-agnostic method by projecting the supervision signals of a teacher model into the student's parameter space. Such projection is implemented by decomposing the training objective into local intermediate targets with approximate mirror descent technique. The proposed method could be less sensitive with the quirks during optimization which could result in a better local optima. Experiments on both image and text datasets show that our proposed proKT consistently achieves the state-of-the-art performance comparing to all existing knowledge distillation methods. | Reject |
ICLR.cc/2021/Conference | Model Compression via Hyper-Structure Network | In this paper, we propose a novel channel pruning method to solve the problem of compression and acceleration of Convolutional Neural Networks (CNNs). Previous channel pruning methods usually ignore the relationships between channels and layers. Many of them parameterize each channel independently by using gates or similar concepts. To fill this gap, a hyper-structure network is proposed to generate the architecture of the main network. Like the existing hypernet, our hyper-structure network can be optimized by regular backpropagation. Moreover, we use a regularization term to specify the computational resource of the compact network. Usually, FLOPs is used as the criterion of computational resource. However, if FLOPs is used in the regularization, it may over penalize early layers. To address this issue, we further introduce learnable layer-wise scaling factors to balance the gradients from different terms, and they can be optimized by hyper-gradient descent. Extensive experimental results on CIFAR-10 and ImageNet show that our method is competitive with state-of-the-art methods. | Reject |
ICLR.cc/2022/Conference | Multi-Agent MDP Homomorphic Networks | This paper introduces Multi-Agent MDP Homomorphic Networks, a class of networks that allows distributed execution using only local information, yet is able to share experience between global symmetries in the joint state-action space of cooperative multi-agent systems. In cooperative multi-agent systems, complex symmetries arise between different configurations of the agents and their local observations. For example, consider a group of agents navigating: rotating the state globally results in a permutation of the optimal joint policy. Existing work on symmetries in single agent reinforcement learning can only be generalized to the fully centralized setting, because such approaches rely on the global symmetry in the full state-action spaces, and these can result in correspondences across agents. To encode such symmetries while still allowing distributed execution we propose a factorization that decomposes global symmetries into local transformations. Our proposed factorization allows for distributing the computation that enforces global symmetries over local agents and local interactions. We introduce a multi-agent equivariant policy network based on this factorization. We show empirically on symmetric multi-agent problems that globally symmetric distributable policies improve data efficiency compared to non-equivariant baselines. | Accept (Poster) |
ICLR.cc/2023/Conference | Waveformer: Linear-Time Attention with Forward and Backward Wavelet Transform | We propose Waveformer that learns attention mechanism in the wavelet coefficient space, requires only linear time complexity, and enjoys universal approximating power. Specifically, we first apply forward wavelet transform to project the input sequences to multi-resolution orthogonal wavelet bases, then conduct nonlinear transformations (in this case, a random feature kernel) in the wavelet coefficient space, and finally reconstruct the representation in input space via backward wavelet transform. We note that other non-linear transformations may be used, hence we name the learning paradigm Wavelet transformatIon for Sequence lEarning (WISE). We emphasize the importance of backward reconstruction in the WISE paradigm — without it, one would be mixing information from both the input space and coefficient space through skip connections, which shall not be considered as mathematically sound. Compared with Fourier transform in recent works, wavelet transform is more efficient in time complexity and better captures local and positional information; we further support this through our ablation studies. Extensive experiments on seven long-range understanding datasets from the Long Range Arena benchmark and code understanding tasks demonstrate that (1) Waveformer achieves competitive and even better accuracy than a number of state-of-the-art Transformer variants and (2) WISE can boost accuracies of various attention approximation methods without increasing the time complexity. These together showcase the superiority of learning attention in a wavelet coefficient space over the input space. | Reject |
ICLR.cc/2023/Conference | Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts | Training machine learning models robust to distribution shifts is critical for real-world applications. Some robust training algorithms (e.g., Group DRO) specialize to group shifts and require group information on all training points. Other methods (e.g., CVaR DRO) that do not need group annotations can be overly conservative, since they naively upweight high loss points which may form a contrived set that does not correspond to any meaningful group in the real world (e.g., when the high loss points are randomly mislabeled training points). In this work, we address limitations in prior approaches by assuming a more nuanced form of group shift: conditioned on the label, we assume that the true group function (indicator over group) is simple. For example, we may expect that group shifts occur along low bitrate features (e.g., image background, lighting). Thus, we aim to learn a model that maintains high accuracy on simple group functions realized by these low bitrate features, that need not spend valuable model capacity achieving high accuracy on contrived groups of examples. Based on this, we consider the two-player game formulation of DRO where the adversary's capacity is bitrate-constrained. Our resulting practical algorithm, Bitrate-Constrained DRO (\bdro), does not require group information on training samples yet matches the performance of Group DRO on datasets that have training group annotations and that of CVaR DRO on long-tailed distributions. Our theoretical analysis reveals that in some settings \bdro objective can provably yield statistically efficient and less conservative solutions than unconstrained CVaR DRO. | Accept: poster |
ICLR.cc/2022/Conference | Constraint-based graph network simulator | In the rapidly advancing area of learned physical simulators, nearly all methods train a forward model that directly predicts future states from input states. However, many traditional simulation engines use a constraint-based approach instead of direct prediction. Here we present a framework for constraint-based learned simulation, where a scalar constraint function is implemented as a trainable function approximator, and future predictions are computed as the solutions to a constraint satisfaction problem. We implement our method using a graph neural network as the constraint function and gradient descent as the constraint solver. The architecture can be trained by standard backpropagation. We test the model on a variety of challenging physical domains, including simulated ropes, bouncing balls, colliding irregular shapes and splashing fluids. Our model achieves better or comparable performance to top learned simulators. A key advantage of our model is the ability to generalize to more solver iterations at test time to improve the simulation accuracy. We also show how hand-designed constraints can be added at test time to satisfy objectives which were not present in the training data, which is not possible with forward approaches. Our constraint-based framework is applicable to any setting in which forward learned simulators are used, and more generally demonstrates key ways that learned models can leverage popular methods in numerical methods. | Reject |