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R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning | https://openreview.net/forum?id=2eXhNpHeW6E | https://openreview.net/forum?id=2eXhNpHeW6E | Shengyao Lu,Bang Liu,Keith G Mills,SHANGLING JUI,Di Niu | ICLR 2022,Spotlight | Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules. | https://openreview.net/pdf/08116e08b73b5c728213b5d350ddbbcf4154bb9f.pdf |
Representation Learning for Online and Offline RL in Low-rank MDPs | https://openreview.net/forum?id=J4iSIR9fhY0 | https://openreview.net/forum?id=J4iSIR9fhY0 | Masatoshi Uehara,Xuezhou Zhang,Wen Sun | ICLR 2022,Spotlight | This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL procedures such as exploration and exploitation, in a sample efficient manner. We focus on the low-rank Markov Decision Processes (MDPs) where the transition dynamics correspond to a low-rank transition matrix. Unlike prior works that assume the representation is known (e.g., linear MDPs), here we need to learn the representation for the low-rank MDP. We study both the online RL and offline RL settings. For the online setting, operating with the same computational oracles used in FLAMBE (Agarwal et.al), the state-of-art algorithm for learning representations in low-rank MDPs, we propose an algorithm REP-UCB Upper Confidence Bound driven Representation learning for RL), which significantly improves the sample complexity from $\widetilde{O}( A^9 d^7 / (\epsilon^{10} (1-\gamma)^{22}))$ for FLAMBE to $\widetilde{O}( A^4 d^4 / (\epsilon^2 (1-\gamma)^{2}) )$ with $d$ being the rank of the transition matrix (or dimension of the ground truth representation), $A$ being the number of actions, and $\gamma$ being the discounted factor. Notably, REP-UCB is simpler than FLAMBE, as it directly balances the interplay between representation learning, exploration, and exploitation, while FLAMBE is an explore-then-commit style approach and has to perform reward-free exploration step-by-step forward in time. For the offline RL setting, we develop an algorithm that leverages pessimism to learn under a partial coverage condition: our algorithm is able to compete against any policy as long as it is covered by the offline distribution. | https://openreview.net/pdf/5efca5979678883d593d4e2a7fd7c48f0add1015.pdf |
Lossless Compression with Probabilistic Circuits | https://openreview.net/forum?id=X_hByk2-5je | https://openreview.net/forum?id=X_hByk2-5je | Anji Liu,Stephan Mandt,Guy Van den Broeck | ICLR 2022,Spotlight | Despite extensive progress on image generation, common deep generative model architectures are not easily applied to lossless compression. For example, VAEs suffer from a compression cost overhead due to their latent variables. This overhead can only be partially eliminated with elaborate schemes such as bits-back coding, often resulting in poor single-sample compression rates. To overcome such problems, we establish a new class of tractable lossless compression models that permit efficient encoding and decoding: Probabilistic Circuits (PCs). These are a class of neural networks involving $|p|$ computational units that support efficient marginalization over arbitrary subsets of the $D$ feature dimensions, enabling efficient arithmetic coding. We derive efficient encoding and decoding schemes that both have time complexity $\mathcal{O} (\log(D) \cdot |p|)$, where a naive scheme would have linear costs in $D$ and $|p|$, making the approach highly scalable. Empirically, our PC-based (de)compression algorithm runs 5-40 times faster than neural compression algorithms that achieve similar bitrates. By scaling up the traditional PC structure learning pipeline, we achieve state-of-the-art results on image datasets such as MNIST. Furthermore, PCs can be naturally integrated with existing neural compression algorithms to improve the performance of these base models on natural image datasets. Our results highlight the potential impact that non-standard learning architectures may have on neural data compression. | https://openreview.net/pdf/7cccb2cf8c807b3d5eeee9e05f70c8b5ea9ab246.pdf |
Understanding Domain Randomization for Sim-to-real Transfer | https://openreview.net/forum?id=T8vZHIRTrY | https://openreview.net/forum?id=T8vZHIRTrY | Xiaoyu Chen,Jiachen Hu,Chi Jin,Lihong Li,Liwei Wang | ICLR 2022,Spotlight | Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization---one of the most popular algorithms for sim-to-real transfer---has been demonstrated to be effective in various tasks in robotics and autonomous driving. Despite its empirical successes, theoretical understanding on why this simple algorithm works is largely missing. In this paper, we propose a theoretical framework for sim-to-real transfers, in which the simulator is modeled as a set of MDPs with tunable parameters (corresponding to unknown physical parameters such as friction). We provide sharp bounds on the sim-to-real gap---the difference between the value of policy returned by domain randomization and the value of an optimal policy for the real world. We prove that sim-to-real transfer can succeed under mild conditions without any real-world training samples. Our theory also highlights the importance of using memory (i.e., history-dependent policies) in domain randomization. Our proof is based on novel techniques that reduce the problem of bounding the sim-to-real gap to the problem of designing efficient learning algorithms for infinite-horizon MDPs, which we believe are of independent interest. | https://openreview.net/pdf/4e09871da1715277fa6f29c516b944b9e97b0c16.pdf |
$\mathrm{SO}(2)$-Equivariant Reinforcement Learning | https://openreview.net/forum?id=7F9cOhdvfk_ | https://openreview.net/forum?id=7F9cOhdvfk_ | Dian Wang,Robin Walters,Robert Platt | ICLR 2022,Spotlight | Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function. Such models are applicable to robotic manipulation learning which can often be formulated as a rotationally symmetric problem. This paper studies equivariant model architectures in the context of $Q$-learning and actor-critic reinforcement learning. We identify equivariant and invariant characteristics of the optimal $Q$-function and the optimal policy and propose equivariant DQN and SAC algorithms that leverage this structure. We present experiments that demonstrate that our equivariant versions of DQN and SAC can be significantly more sample efficient than competing algorithms on an important class of robotic manipulation problems. | https://openreview.net/pdf/9f58959cef1dc2c685298e532713a5104f2df44b.pdf |
Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption | https://openreview.net/forum?id=CuV_qYkmKb3 | https://openreview.net/forum?id=CuV_qYkmKb3 | Dara Bahri,Heinrich Jiang,Yi Tay,Donald Metzler | ICLR 2022,Spotlight | Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders of magnitude less labeled data. However, such methods are domain-specific and little has been done to leverage this technique on real-world \emph{tabular} datasets. We propose \textsc{Scarf}, a simple, widely-applicable technique for contrastive learning, where views are formed by corrupting a random subset of features. When applied to pre-train deep neural networks on the 69 real-world, tabular classification datasets from the OpenML-CC18 benchmark, \textsc{Scarf} not only improves classification accuracy in the fully-supervised setting but does so also in the presence of label noise and in the semi-supervised setting where only a fraction of the available training data is labeled. We show that \textsc{Scarf} complements existing strategies and outperforms alternatives like autoencoders. We conduct comprehensive ablations, detailing the importance of a range of factors. | https://openreview.net/pdf/b63986fb73f0b81b950ec3c4c84a0977ad6bdee1.pdf |
Responsible Disclosure of Generative Models Using Scalable Fingerprinting | https://openreview.net/forum?id=sOK-zS6WHB | https://openreview.net/forum?id=sOK-zS6WHB | Ning Yu,Vladislav Skripniuk,Dingfan Chen,Larry S. Davis,Mario Fritz | ICLR 2022,Spotlight | Over the past years, deep generative models have achieved a new level of performance. Generated data has become difficult, if not impossible, to be distinguished from real data. While there are plenty of use cases that benefit from this technology, there are also strong concerns on how this new technology can be misused to generate deep fakes and enable misinformation at scale. Unfortunately, current deep fake detection methods are not sustainable, as the gap between real and fake continues to close. In contrast, our work enables a responsible disclosure of such state-of-the-art generative models, that allows model inventors to fingerprint their models, so that the generated samples containing a fingerprint can be accurately detected and attributed to a source. Our technique achieves this by an efficient and scalable ad-hoc generation of a large population of models with distinct fingerprints. Our recommended operation point uses a 128-bit fingerprint which in principle results in more than 10^{38} identifiable models. Experiments show that our method fulfills key properties of a fingerprinting mechanism and achieves effectiveness in deep fake detection and attribution. Code and models are available at https://github.com/ningyu1991/ScalableGANFingerprints. | https://openreview.net/pdf/e17ae78a19a967b27b17c7545fd71dfff5109784.pdf |
Path Auxiliary Proposal for MCMC in Discrete Space | https://openreview.net/forum?id=JSR-YDImK95 | https://openreview.net/forum?id=JSR-YDImK95 | Haoran Sun,Hanjun Dai,Wei Xia,Arun Ramamurthy | ICLR 2022,Spotlight | Energy-based Model (EBM) offers a powerful approach for modeling discrete structure, but both inference and learning of EBM are hard as it involves sampling from discrete distributions. Recent work shows Markov Chain Monte Carlo (MCMC) with the informed proposal is a powerful tool for such sampling. However, an informed proposal only allows local updates as it requires evaluating all energy changes in the neighborhood.
In this work, we present a path auxiliary algorithm that uses a composition of local moves to efficiently explore large neighborhoods. We also give a fast version of our algorithm that only queries the evaluation of energy function twice for each proposal via linearization of the energy function. Empirically, we show that our path auxiliary algorithms considerably outperform other generic samplers on various discrete models for sampling, inference, and learning. Our method can also be used to train deep EBMs for high-dimensional discrete data. | https://openreview.net/pdf/03e5897fe57ea115623975dd15807e2738f12501.pdf |
Possibility Before Utility: Learning And Using Hierarchical Affordances | https://openreview.net/forum?id=7b4zxUnrO2N | https://openreview.net/forum?id=7b4zxUnrO2N | Robby Costales,Shariq Iqbal,Fei Sha | ICLR 2022,Spotlight | Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead only consider ones they deem possible in the first place. By focusing only on what is feasible, or "afforded'', at the present moment, an agent can spend more time both evaluating the utility of and acting on what matters. To this end, we present Hierarchical Affordance Learning (HAL), a method that learns a model of hierarchical affordances in order to prune impossible subtasks for more effective learning. Existing works in hierarchical reinforcement learning provide agents with structural representations of subtasks but are not affordance-aware, and by grounding our definition of hierarchical affordances in the present state, our approach is more flexible than the multitude of approaches that ground their subtask dependencies in a symbolic history. While these logic-based methods often require complete knowledge of the subtask hierarchy, our approach is able to utilize incomplete and varying symbolic specifications. Furthermore, we demonstrate that relative to non-affordance-aware methods, HAL agents are better able to efficiently learn complex tasks, navigate environment stochasticity, and acquire diverse skills in the absence of extrinsic supervision---all of which are hallmarks of human learning. | https://openreview.net/pdf/f4b5c96c2948ff7fcea521e9713644691c27bab2.pdf |
Interpretable Unsupervised Diversity Denoising and Artefact Removal | https://openreview.net/forum?id=DfMqlB0PXjM | https://openreview.net/forum?id=DfMqlB0PXjM | Mangal Prakash,Mauricio Delbracio,Peyman Milanfar,Florian Jug | ICLR 2022,Spotlight | Image denoising and artefact removal are complex inverse problems admitting multiple valid solutions. Unsupervised diversity restoration, that is, obtaining a diverse set of possible restorations given a corrupted image, is important for ambiguity removal in many applications such as microscopy where paired data for supervised training are often unobtainable. In real world applications, imaging noise and artefacts are typically hard to model, leading to unsatisfactory performance of existing unsupervised approaches. This work presents an interpretable approach for unsupervised and diverse image restoration. To this end, we introduce a capable architecture called Hierarchical DivNoising (HDN) based on hierarchical Variational Autoencoder. We show that HDN learns an interpretable multi-scale representation of artefacts and we leverage this interpretability to remove imaging artefacts commonly occurring in microscopy data. Our method achieves state-of-the-art results on twelve benchmark image denoising datasets while providing access to a whole distribution of sensibly restored solutions.
Additionally, we demonstrate on three real microscopy datasets that HDN removes artefacts without supervision, being the first method capable of doing so while generating multiple plausible restorations all consistent with the given corrupted image. | https://openreview.net/pdf/3fb037cb33af2bbe50cc241272e4f9313aaf3552.pdf |
Half-Inverse Gradients for Physical Deep Learning | https://openreview.net/forum?id=HTx7vrlLBEj | https://openreview.net/forum?id=HTx7vrlLBEj | Patrick Schnell,Philipp Holl,Nils Thuerey | ICLR 2022,Spotlight | Recent works in deep learning have shown that integrating differentiable physics simulators into the training process can greatly improve the quality of results. Although this combination represents a more complex optimization task than usual neural network training, the same gradient-based optimizers are used to minimize the loss function. However, the integrated physics solvers have a profound effect on the gradient flow as manipulating scales in magnitude and direction is an inherent property of many physical processes. Consequently, the gradient flow is often highly unbalanced and creates an environment in which existing gradient-based optimizers perform poorly. In this work, we analyze the characteristics of both physical and neural network optimizations separately to derive a new method based on a half-inversion of the Jacobian. Our approach combines principles of both classical network and physics optimizers to solve the combined optimization task. Compared to state-of-the-art neural network optimizers, our method converges more quickly and to better solutions, which we demonstrate on three complex learning problems involving nonlinear oscillators, the Schroedinger equation and the Poisson problem. | https://openreview.net/pdf/30d8e38a4f776ba1a0480b58ae7a48fc34a42760.pdf |
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits | https://openreview.net/forum?id=X_ch3VrNSRg | https://openreview.net/forum?id=X_ch3VrNSRg | Yikun Ban,Yuchen Yan,Arindam Banerjee,Jingrui He | ICLR 2022,Spotlight | In this paper, we propose a novel neural exploration strategy in contextual bandits, EE-Net, distinct from the standard UCB-based and TS-based approaches. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration tradeoff in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confidence Bound (UCB). In recent literature, linear contextual bandits have adopted ridge regression to estimate the reward function and combine it with TS or UCB strategies for exploration. However, this line of works explicitly assumes the reward is based on a linear function of arm vectors, which may not be true in real-world datasets. To overcome this challenge, a series of neural bandit algorithms have been proposed, where a neural network is used to learn the underlying reward function and TS or UCB are adapted for exploration. Instead of calculating a large-deviation based statistical bound for exploration like previous methods, we propose "EE-Net", a novel neural-based exploration strategy. In addition to using a neural network (Exploitation network) to learn the reward function, EE-Net uses another neural network (Exploration network) to adaptively learn potential gains compared to the currently estimated reward for exploration. Then, a decision-maker is constructed to combine the outputs from the Exploitation and Exploration networks. We prove that EE-Net can achieve $\mathcal{O}(\sqrt{T\log T})$ regret and show that EE-Net outperforms existing linear and neural contextual bandit baselines on real-world datasets. | https://openreview.net/pdf/c6228ff8fe747650e5b549f73f34d2306402b787.pdf |
Spike-inspired rank coding for fast and accurate recurrent neural networks | https://openreview.net/forum?id=iMH1e5k7n3L | https://openreview.net/forum?id=iMH1e5k7n3L | Alan Jeffares,Qinghai Guo,Pontus Stenetorp,Timoleon Moraitis | ICLR 2022,Spotlight | Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. in the rank order in which neurons fire, whereas artificial neural networks (ANNs) conventionally do not. As a result, models of SNNs for neuromorphic computing are regarded as potentially more rapid and efficient than ANNs when dealing with temporal input. On the other hand, ANNs are simpler to train, and usually achieve superior performance. Here we show that temporal coding such as rank coding (RC) inspired by SNNs can also be applied to conventional ANNs such as LSTMs, and leads to computational savings and speedups.
In our RC for ANNs, we apply backpropagation through time using the standard real-valued activations, but only from a strategically early time step of each sequential input example, decided by a threshold-crossing event. Learning then incorporates naturally also when to produce an output, without other changes to the model or the algorithm. Both the forward and the backward training pass can be significantly shortened by skipping the remaining input sequence after that first event. RC-training also significantly reduces time-to-insight during inference, with a minimal decrease in accuracy. The desired speed-accuracy trade-off is tunable by varying the threshold or a regularization parameter that rewards output entropy. We demonstrate these in two toy problems of sequence classification, and in a temporally-encoded MNIST dataset where our RC model achieves 99.19% accuracy after the first input time-step, outperforming the state of the art in temporal coding with SNNs, as well as in spoken-word classification of Google Speech Commands, outperforming non-RC-trained early inference with LSTMs. | https://openreview.net/pdf/c1c6fc25f1bbf5574f5a49723ca38dafe70660ca.pdf |
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective | https://openreview.net/forum?id=W9G_ImpHlQd | https://openreview.net/forum?id=W9G_ImpHlQd | Yimeng Zhang,Yuguang Yao,Jinghan Jia,Jinfeng Yi,Mingyi Hong,Shiyu Chang,Sijia Liu | ICLR 2022,Spotlight | The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustifying ML models against adversarial attacks is now a major focus of research. However, nearly all existing defense methods, particularly for robust training, made the white-box assumption that the defender has the access to the details of an ML model (or its surrogate alternatives if available), e.g., its architectures and parameters. Beyond existing works, in this paper we aim to address the problem of black-box defense: How to robustify a black-box model using just input queries and output feedback? Such a problem arises in practical scenarios, where the owner of the predictive model is reluctant to share model information in order to preserve privacy. To this end, we propose a general notion of defensive operation that can be applied to black-box models, and design it through the lens of denoised smoothing (DS), a first-order (FO) certified defense technique. To allow the design of merely using model queries, we further integrate DS with the zeroth-order (gradient-free) optimization. However, a direct implementation of zeroth-order (ZO) optimization suffers a high variance of gradient estimates, and thus leads to ineffective defense. To tackle this problem, we next propose to prepend an autoencoder (AE) to a given (black-box) model so that DS can be trained using variance-reduced ZO optimization. We term the eventual defense as ZO-AE-DS. In practice, we empirically show that ZO-AE-DS can achieve improved accuracy, certified robustness, and query complexity over existing baselines. And the effectiveness of our approach is justified under both image classification and image reconstruction tasks. | https://openreview.net/pdf/892f86851a58432caba514751380a19f53458d67.pdf |
RelaxLoss: Defending Membership Inference Attacks without Losing Utility | https://openreview.net/forum?id=FEDfGWVZYIn | https://openreview.net/forum?id=FEDfGWVZYIn | Dingfan Chen,Ning Yu,Mario Fritz | ICLR 2022,Spotlight | As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models.
Existing works evidence strong connection between the distinguishability of the training and testing loss distributions and the model's vulnerability to MIAs. Motivated by existing results, we propose a novel training framework based on a relaxed loss ($\textbf{RelaxLoss}$) with a more achievable learning target, which leads to narrowed generalization gap and reduced privacy leakage. RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead. Through extensive evaluations on five datasets with diverse modalities (images, medical data, transaction records), our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs as well as model utility. Our defense is the first that can withstand a wide range of attacks while preserving (or even improving) the target model's utility. | https://openreview.net/pdf/e3ac303ac886fc33aba9568f6cb7a74e2c021f00.pdf |
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation | https://openreview.net/forum?id=eBS-3YiaIL- | https://openreview.net/forum?id=eBS-3YiaIL- | Bingbin Liu,Elan Rosenfeld,Pradeep Kumar Ravikumar,Andrej Risteski | ICLR 2022,Spotlight | Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models. It has been empirically observed that the choice of the noise distribution is crucial for NCE’s performance. However, such observation has never been made formal or quantitative. In fact, it is not even clear whether the difficulties arising from a poorly chosen noise distribution are statistical or algorithmic in nature.
In this work, we formally pinpoint reasons for NCE’s poor performance when an inappropriate noise distribution is used. Namely, we prove these challenges arise due to an ill-behaved (more precisely, flat) loss landscape.
To address this, we introduce a variant of NCE called \emph{eNCE} which uses an exponential loss and for which \emph{normalized gradient descent} addresses the landscape issues \emph{provably} when the target and noise distributions are in a given exponential family. | https://openreview.net/pdf/6fece6afb2eea04bf01bd8ff7ec9da4a780eb660.pdf |
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics | https://openreview.net/forum?id=mmUA7_O9mjY | https://openreview.net/forum?id=mmUA7_O9mjY | Sizhe Li,Zhiao Huang,Tao Du,Hao Su,Joshua B. Tenenbaum,Chuang Gan | ICLR 2022,Spotlight | Differentiable physics has recently been shown as a powerful tool for solving soft-body manipulation tasks. However, the differentiable physics solver often gets stuck when the initial contact points of the end effectors are sub-optimal or when performing multi-stage tasks that require contact point switching, which often leads to local minima.
To address this challenge, we propose a contact point discovery approach (CPDeform) that guides the stand-alone differentiable physics solver to deform various soft-body plasticines. The key idea of our approach is to integrate optimal transport-based contact points discovery into the differentiable physics solver to overcome the local minima from initial contact points or contact switching.
On single-stage tasks, our method can automatically find suitable initial contact points based on transport priorities. On complex multi-stage tasks, we can iteratively switch the contact points of end-effectors based on transport priorities. To evaluate the effectiveness of our method, we introduce PlasticineLab-M that extends the existing differentiable physics benchmark PlasticineLab to seven new challenging multi-stage soft-body manipulation tasks. Extensive experimental results suggest that: 1) on multi-stage tasks that are infeasible for the vanilla differentiable physics solver, our approach discovers contact points that efficiently guide the solver to completion; 2) on tasks where the vanilla solver performs sub-optimally or near-optimally, our contact point discovery method performs better than or on par with the manipulation performance obtained with handcrafted contact points.
| https://openreview.net/pdf/21c3d98f02351425ae7c7201ec763c603b24b4c7.pdf |
Leveraging Automated Unit Tests for Unsupervised Code Translation | https://openreview.net/forum?id=cmt-6KtR4c4 | https://openreview.net/forum?id=cmt-6KtR4c4 | Baptiste Roziere,Jie Zhang,Francois Charton,Mark Harman,Gabriel Synnaeve,Guillaume Lample | ICLR 2022,Spotlight | With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine translation approaches rely on back-translation, a method developed in the context of natural language translation and one that inherently involves training on noisy inputs. Unfortunately, source code is highly sensitive to small changes; a single token can result in compilation failures or erroneous programs, unlike natural languages where small inaccuracies may not change the meaning of a sentence. To address this issue, we propose to leverage an automated unit-testing system to filter out invalid translations, thereby creating a fully tested parallel corpus. We found that fine-tuning an unsupervised model with this filtered data set significantly reduces the noise in the translations so-generated, comfortably outperforming the state-of-the-art for all language pairs studied. In particular, for Java→Python and Python→C++ we outperform the best previous methods by more than 16% and 24% respectively, reducing the error rate by more than 35%. | https://openreview.net/pdf/5afda866e17de3287b9281461435fdc488309beb.pdf |
Scalable Sampling for Nonsymmetric Determinantal Point Processes | https://openreview.net/forum?id=BB4e8Atc1eR | https://openreview.net/forum?id=BB4e8Atc1eR | Insu Han,Mike Gartrell,Jennifer Gillenwater,Elvis Dohmatob,amin karbasi | ICLR 2022,Spotlight | A determinantal point process (DPP) on a collection of $M$ items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items. Recent work shows that removing the kernel symmetry constraint, yielding nonsymmetric DPPs (NDPPs), can lead to significant predictive performance gains for machine learning applications. However, existing work leaves open the question of scalable NDPP sampling. There is only one known DPP sampling algorithm, based on Cholesky decomposition, that can directly apply to NDPPs as well. Unfortunately, its runtime is cubic in $M$, and thus does not scale to large item collections. In this work, we first note that this algorithm can be transformed into a linear-time one for kernels with low-rank structure. Furthermore, we develop a scalable sublinear-time rejection sampling algorithm by constructing a novel proposal distribution. Additionally, we show that imposing certain structural constraints on the NDPP kernel enables us to bound the rejection rate in a way that depends only on the kernel rank. In our experiments we compare the speed of all of these samplers for a variety of real-world tasks. | https://openreview.net/pdf/b38ff838b862c1f5918c345f4322281132fa0715.pdf |
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design | https://openreview.net/forum?id=FRxhHdnxt1 | https://openreview.net/forum?id=FRxhHdnxt1 | Wenhao Gao,Rocío Mercado,Connor W. Coley | ICLR 2022,Spotlight | Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding. This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes, demonstrating the potential to solve both problems of design and synthesis simultaneously. The approach leverages neural networks to probabilistically model the synthetic trees, one reaction step at a time, according to reactivity rules encoded in a discrete action space of reaction templates. We train these networks on hundreds of thousands of artificial pathways generated from a pool of purchasable compounds and a list of expert-curated templates. We validate our method with (a) the recovery of molecules using conditional generation, (b) the identification of synthesizable structural analogs, and (c) the optimization of molecular structures given oracle functions relevant to bioactivity and drug discovery. | https://openreview.net/pdf/9a1a1ce4faf67f9a0f649866c9158c2f0f055db1.pdf |
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design | https://openreview.net/forum?id=LI2bhrE_2A | https://openreview.net/forum?id=LI2bhrE_2A | Wengong Jin,Jeremy Wohlwend,Regina Barzilay,Tommi S. Jaakkola | ICLR 2022,Spotlight | Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities. Previous generative approaches formulate protein design as a structure-conditioned sequence generation task, assuming the desired 3D structure is given a priori. In contrast, we propose to co-design the sequence and 3D structure of CDRs as graphs. Our model unravels a sequence autoregressively while iteratively refining its predicted global structure. The inferred structure in turn guides subsequent residue choices. For efficiency, we model the conditional dependence between residues inside and outside of a CDR in a coarse-grained manner. Our method achieves superior log-likelihood on the test set and outperforms previous baselines in designing antibodies capable of neutralizing the SARS-CoV-2 virus.
| https://openreview.net/pdf/f85e516c2ab137179adf7bda106ae34944694b9d.pdf |
Churn Reduction via Distillation | https://openreview.net/forum?id=HbtFCX2PLq0 | https://openreview.net/forum?id=HbtFCX2PLq0 | Heinrich Jiang,Harikrishna Narasimhan,Dara Bahri,Andrew Cotter,Afshin Rostamizadeh | ICLR 2022,Spotlight | In real-world systems, models are frequently updated as more data becomes available, and in addition to achieving high accuracy, the goal is to also maintain a low difference in predictions compared to the base model (i.e. predictive churn). If model retraining results in vastly different behavior, then it could cause negative effects in downstream systems, especially if this churn can be avoided with limited impact on model accuracy. In this paper, we show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines on a wide range of datasets and model architectures, including fully-connected networks, convolutional networks, and transformers. | https://openreview.net/pdf/d4c4b0da2bc7b1427e642ebdfc966ac7b142ecd0.pdf |
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting | https://openreview.net/forum?id=7twQI5VnC8 | https://openreview.net/forum?id=7twQI5VnC8 | Masahiro Kato,Masaaki Imaizumi,Kenichiro McAlinn,Shota Yasui,Haruo Kakehi | ICLR 2022,Spotlight | We consider learning causal relationships under conditional moment restrictions. Unlike causal inference under unconditional moment restrictions, conditional moment restrictions pose serious challenges for causal inference. To address this issue, we propose a method that transforms conditional moment restrictions to unconditional moment restrictions through importance weighting using a conditional density ratio estimator. Then, using this transformation, we propose a method that successfully estimate a parametric or nonparametric functions defined under the conditional moment restrictions. We analyze the estimation error and provide a bound on the structural function, providing theoretical support for our proposed method. In experiments, we confirm the soundness of our proposed method. | https://openreview.net/pdf/84e1728a94b82499ea64a0d48409de2dfc9115ee.pdf |
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation | https://openreview.net/forum?id=hfU7Ka5cfrC | https://openreview.net/forum?id=hfU7Ka5cfrC | Ross M Clarke,Elre Talea Oldewage,José Miguel Hernández-Lobato | ICLR 2022,Spotlight | Machine learning training methods depend plentifully and intricately on hyperparameters, motivating automated strategies for their optimisation. Many existing algorithms restart training for each new hyperparameter choice, at considerable computational cost. Some hypergradient-based one-pass methods exist, but these either cannot be applied to arbitrary optimiser hyperparameters (such as learning rates and momenta) or take several times longer to train than their base models. We extend these existing methods to develop an approximate hypergradient-based hyperparameter optimiser which is applicable to any continuous hyperparameter appearing in a differentiable model weight update, yet requires only one training episode, with no restarts. We also provide a motivating argument for convergence to the true hypergradient, and perform tractable gradient-based optimisation of independent learning rates for each model parameter. Our method performs competitively from varied random hyperparameter initialisations on several UCI datasets and Fashion-MNIST (using a one-layer MLP), Penn Treebank (using an LSTM) and CIFAR-10 (using a ResNet-18), in time only 2-3x greater than vanilla training. | https://openreview.net/pdf/a7f35bc772a0804247c8631982741afe42ec790e.pdf |
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation | https://openreview.net/forum?id=vrW3tvDfOJQ | https://openreview.net/forum?id=vrW3tvDfOJQ | Vincent Mai,Kaustubh Mani,Liam Paull | ICLR 2022,Spotlight | In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency. As this noise is heteroscedastic, its effects can be mitigated using uncertainty-based weights in the optimization process. Previous methods rely on sampled ensembles, which do not capture all aspects of uncertainty. We provide a systematic analysis of the sources of uncertainty in the noisy supervision that occurs in RL, and introduce inverse-variance RL, a Bayesian framework which combines probabilistic ensembles and Batch Inverse Variance weighting. We propose a method whereby two complementary uncertainty estimation methods account for both the Q-value and the environment stochasticity to better mitigate the negative impacts of noisy supervision. Our results show significant improvement in terms of sample efficiency on discrete and continuous control tasks. | https://openreview.net/pdf/2957fd1597c9d0c85f628e1d53b0aba1a7aa45b1.pdf |
Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining | https://openreview.net/forum?id=O1DEtITim__ | https://openreview.net/forum?id=O1DEtITim__ | Miao Lu,Xiaolong Luo,Tianlong Chen,Wuyang Chen,Dong Liu,Zhangyang Wang | ICLR 2022,Spotlight | We present a novel framework to train a large deep neural network (DNN) for only $\textit{once}$, which can then be pruned to $\textit{any sparsity ratio}$ to preserve competitive accuracy $\textit{without any re-training}$. Conventional methods often require (iterative) pruning followed by re-training, which not only incurs large overhead beyond the original DNN training but also can be sensitive to retraining hyperparameters. Our core idea is to re-cast the DNN training as an explicit $\textit{pruning-aware}$ process: that is formulated with an auxiliary $K$-sparse polytope constraint, to encourage network weights to lie in a convex hull spanned by $K$-sparse vectors, potentially resulting in more sparse weight matrices. We then leverage a stochastic Frank-Wolfe (SFW) algorithm to solve this new constrained optimization, which naturally leads to sparse weight updates each time. We further note an overlooked fact that existing DNN initializations were derived to enhance SGD training (e.g., avoid gradient explosion or collapse), but was unaligned with the challenges of training with SFW. We hence also present the first learning-based initialization scheme specifically for boosting SFW-based DNN training. Experiments on CIFAR-10 and Tiny-ImageNet datasets demonstrate that our new framework named $\textbf{SFW-pruning}$ consistently achieves the state-of-the-art performance on various benchmark DNNs over a wide range of pruning ratios. Moreover, SFW-pruning only needs to train once on the same model and dataset, for obtaining arbitrary ratios, while requiring neither iterative pruning nor retraining. All codes will be released to the public. | https://openreview.net/pdf/c893710fa491c04dc86547df19635fae45a567c7.pdf |
Learning transferable motor skills with hierarchical latent mixture policies | https://openreview.net/forum?id=qTHBE7E9iej | https://openreview.net/forum?id=qTHBE7E9iej | Dushyant Rao,Fereshteh Sadeghi,Leonard Hasenclever,Markus Wulfmeier,Martina Zambelli,Giulia Vezzani,Dhruva Tirumala,Yusuf Aytar,Josh Merel,Nicolas Heess,raia hadsell | ICLR 2022,Spotlight | For robots operating in the real world, it is desirable to learn reusable abstract behaviours that can effectively be transferred across numerous tasks and scenarios.
We propose an approach to learn skills from data using a hierarchical mixture latent variable model.
Our method exploits a multi-level hierarchy of both discrete and continuous latent variables, to model a discrete set of abstract high-level behaviours while allowing for variance in how they are executed.
We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model.
The resulting skills can be transferred to new tasks, unseen objects, and from state to vision-based policies, yielding significantly better sample efficiency and asymptotic performance compared to existing skill- and imitation-based methods.
We also perform further analysis showing how and when the skills are most beneficial: they encourage directed exploration to cover large regions of the state space relevant to the task, making them most effective in challenging sparse-reward settings. | https://openreview.net/pdf/da585a69d336f46f18b80d4a026fd3a7dcb40eae.pdf |
Compositional Training for End-to-End Deep AUC Maximization | https://openreview.net/forum?id=gPvB4pdu_Z | https://openreview.net/forum?id=gPvB4pdu_Z | Zhuoning Yuan,Zhishuai Guo,Nitesh Chawla,Tianbao Yang | ICLR 2022,Spotlight | Recently, deep AUC maximization (DAM) has achieved great success in different domains (e.g., medical image classification). However, the end-to-end training for deep AUC maximization still remains a challenging problem. Previous studies employ an ad-hoc two-stage approach that first trains the network by optimizing a traditional loss (e.g., cross-entropy loss) and then finetunes the network by optimizing an AUC loss. This is because that training a deep neural network from scratch by maximizing an AUC loss usually does not yield a satisfactory performance. This phenomenon can be attributed to the degraded feature representations learned by maximizing the AUC loss from scratch. To address this issue, we propose a novel compositional training framework for end-to-end DAM, namely compositional DAM. The key idea of compositional training is to minimize a compositional objective function, where the outer function corresponds to an AUC loss and the inner function represents a gradient descent step for minimizing a traditional loss, e.g., the cross-entropy (CE) loss. To optimize the non-standard compositional objective, we propose an efficient and provable stochastic optimization algorithm. The proposed algorithm enhances the capabilities of both robust feature learning and robust classifier learning by alternatively taking a gradient descent step for the CE loss and for the AUC loss in a systematic way. We conduct extensive empirical studies on imbalanced benchmark and medical image datasets, which unanimously verify the effectiveness of the proposed method. Our results show that the compositional training approach dramatically improves both the feature representations and the testing AUC score compared with traditional deep learning approaches, and yields better performance than the two-stage approaches for DAM as well. The proposed method is implemented in our open-sourced library LibAUC (https://www.libauc.org) and code is available at https://github.com/Optimization-AI/LibAUC. | https://openreview.net/pdf/a32a2790db0163f6e6f71daa98631818c4713912.pdf |
Explanations of Black-Box Models based on Directional Feature Interactions | https://openreview.net/forum?id=45Mr7LeKR9 | https://openreview.net/forum?id=45Mr7LeKR9 | Aria Masoomi,Davin Hill,Zhonghui Xu,Craig P Hersh,Edwin K. Silverman,Peter J. Castaldi,Stratis Ioannidis,Jennifer Dy | ICLR 2022,Spotlight | As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent. Several recent works explain black-box models by capturing the most influential features for prediction per instance; such explanation methods are univariate, as they characterize importance per feature. We extend univariate explanation to a higher-order; this enhances explainability, as bivariate methods can capture feature interactions in black-box models, represented as a directed graph. Analyzing this graph enables us to discover groups of features that are equally important (i.e., interchangeable), while the notion of directionality allows us to identify the most influential features. We apply our bivariate method on Shapley value explanations, and experimentally demonstrate the ability of directional explanations to discover feature interactions. We show the superiority of our method against state-of-the-art on CIFAR10, IMDB, Census, Divorce, Drug, and gene data. | https://openreview.net/pdf/861d5f0b89fc65fc7bd1c9d4a41c92e697f76061.pdf |
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning | https://openreview.net/forum?id=Fl3Mg_MZR- | https://openreview.net/forum?id=Fl3Mg_MZR- | Marc Vischer,Robert Tjarko Lange,Henning Sprekeler | ICLR 2022,Spotlight | The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning. But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems? In this work, we address this question by comparing sparse agents who have to address the non-stationarity of the exploration-exploitation problem with supervised agents trained to imitate an expert. We show that feed-forward networks trained with behavioural cloning compared to reinforcement learning can be pruned to higher levels of sparsity without performance degradation. This suggests that in order to solve the RL-specific distributional shift agents require more degrees of freedom. Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in both learning paradigms can be attributed to the identified mask rather than the weight initialization. The input layer mask selectively prunes entire input dimensions that turn out to be irrelevant for the task at hand. At a moderate level of sparsity the mask identified by iterative magnitude pruning yields minimal task-relevant representations, i.e., an interpretable inductive bias. Finally, we propose a simple initialization rescaling which promotes the robust identification of sparse task representations in low-dimensional control tasks. | https://openreview.net/pdf/8e8fd56ca3b46bba9d6db9d68f6fc7df8c828705.pdf |
Self-supervised Learning is More Robust to Dataset Imbalance | https://openreview.net/forum?id=4AZz9osqrar | https://openreview.net/forum?id=4AZz9osqrar | Hong Liu,Jeff Z. HaoChen,Adrien Gaidon,Tengyu Ma | ICLR 2022,Spotlight | Self-supervised learning (SSL) is a scalable way to learn general visual representations since it learns without labels. However, large-scale unlabeled datasets in the wild often have long-tailed label distributions, where we know little about the behavior of SSL. In this work, we systematically investigate self-supervised learning under dataset imbalance. First, we find via extensive experiments that off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. The performance gap between balanced and imbalanced pre-training with SSL is significantly smaller than the gap with supervised learning, across sample sizes, for both in-domain and, especially, out-of-domain evaluation. Second, towards understanding the robustness of SSL, we hypothesize that SSL learns richer features from frequent data: it may learn label-irrelevant-but-transferable features that help classify the rare classes and downstream tasks. In contrast, supervised learning has no incentive to learn features irrelevant to the labels from frequent examples. We validate this hypothesis with semi-synthetic experiments as well as rigorous mathematical analyses on a simplified setting. Third, inspired by the theoretical insights, we devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets with several evaluation criteria, closing the small gap between balanced and imbalanced datasets with the same number of examples. | https://openreview.net/pdf/8dbaf8d4a30f70cb8b4967ee6b1814c513bc92e6.pdf |
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions | https://openreview.net/forum?id=apv504XsysP | https://openreview.net/forum?id=apv504XsysP | Nicholas Gao,Stephan Günnemann | ICLR 2022,Spotlight | Solving the Schrödinger equation is key to many quantum mechanical properties. However, an analytical solution is only tractable for single-electron systems. Recently, neural networks succeeded at modelling wave functions of many-electron systems. Together with the variational Monte-Carlo (VMC) framework, this led to solutions on par with the best known classical methods. Still, these neural methods require tremendous amounts of computational resources as one has to train a separate model for each molecular geometry. In this work, we combine a Graph Neural Network (GNN) with a neural wave function to simultaneously solve the Schrödinger equation for multiple geometries via VMC. This enables us to model continuous subsets of the potential energy surface with a single training pass. Compared to existing state-of-the-art networks, our Potential Energy Surface Network (PESNet) speeds up training for multiple geometries by up to 40 times while matching or surpassing their accuracy. This may open the path to accurate and orders of magnitude cheaper quantum mechanical calculations. | https://openreview.net/pdf/c55015744159581849683b350d34f68681b90315.pdf |
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver | https://openreview.net/forum?id=SidzxAb9k30 | https://openreview.net/forum?id=SidzxAb9k30 | Xiaoyu Chen,Jiachen Hu,Lin Yang,Liwei Wang | ICLR 2022,Spotlight | Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to couple tightly with the model-learning procedure. Hence the learned models may lack the ability of being re-used with more specialized planners. In this paper we address this issue and provide approaches to learn an RL model efficiently without the guidance of a reward signal. In particular, we take a plug-in solver approach, where we focus on learning a model in the exploration phase and demand that \emph{any planning algorithm} on the learned model can give a near-optimal policy. Specicially, we focus on the linear mixture MDP setting, where the probability transition matrix is a (unknown) convex combination of a set of existing models. We show that, by establishing a novel exploration algorithm, the plug-in approach learns a model by taking $\tilde{O}(d^2H^3/\epsilon^2)$ interactions with the environment and \emph{any} $\epsilon$-optimal planner on the model gives an $O(\epsilon)$-optimal policy on the original model. This sample complexity matches lower bounds for non-plug-in approaches and is \emph{statistically optimal}. We achieve this result by leveraging a careful maximum total-variance bound using Bernstein inequality and properties specified to linear mixture MDP. | https://openreview.net/pdf/91c23b5592d358e7283f6fdfe3f0cf6890b65e1c.pdf |
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data | https://openreview.net/forum?id=MEpKGLsY8f | https://openreview.net/forum?id=MEpKGLsY8f | Haoang Chi,Feng Liu,Wenjing Yang,Long Lan,Tongliang Liu,Bo Han,Gang Niu,Mingyuan Zhou,Masashi Sugiyama | ICLR 2022,Spotlight | In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However, the implicit assumptions behind NCD are still unclear. In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes. Based on this finding, NCD is theoretically solvable under certain assumptions and can be naturally linked to meta-learning that has exactly the same assumption as NCD. Thus, we can empirically solve the NCD problem by meta-learning algorithms after slight modifications. This meta-learning-based methodology significantly reduces the amount of unlabeled data needed for training and makes it more practical, as demonstrated in experiments. The use of very limited data is also justified by the application scenario of NCD: since it is unnatural to label only seen-class data, NCD is sampling instead of labeling in causality. Therefore, unseen-class data should be collected on the way of collecting seen-class data, which is why they are novel and first need to be clustered. | https://openreview.net/pdf/ce36270eda861ce89f8998343017db1dff96ed19.pdf |
Constrained Policy Optimization via Bayesian World Models | https://openreview.net/forum?id=PRZoSmCinhf | https://openreview.net/forum?id=PRZoSmCinhf | Yarden As,Ilnura Usmanova,Sebastian Curi,Andreas Krause | ICLR 2022,Spotlight | Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based approach for policy optimization in safety critical tasks modeled via constrained Markov decision processes. Our approach utilizes Bayesian world models, and harnesses the resulting uncertainty to maximize optimistic upper bounds on the task objective, as well as pessimistic upper bounds on the safety constraints. We demonstrate LAMBDA's state of the art performance on the Safety-Gym benchmark suite in terms of sample efficiency and constraint violation. | https://openreview.net/pdf/649d2990399ada19288169dd3031ecbb109a02aa.pdf |
VAE Approximation Error: ELBO and Exponential Families | https://openreview.net/forum?id=OIs3SxU5Ynl | https://openreview.net/forum?id=OIs3SxU5Ynl | Alexander Shekhovtsov,Dmitrij Schlesinger,Boris Flach | ICLR 2022,Spotlight | The importance of Variational Autoencoders reaches far beyond standalone generative models -- the approach is also used for learning latent representations and can be generalized to semi-supervised learning. This requires a thorough analysis of their commonly known shortcomings: posterior collapse and approximation errors. This paper analyzes VAE approximation errors caused by the combination of the ELBO objective and encoder models from conditional exponential families, including, but not limited to, commonly used conditionally independent discrete and continuous models.
We characterize subclasses of generative models consistent with these encoder families. We show that the ELBO optimizer is pulled away from the likelihood optimizer towards the consistent subset and study this effect experimentally. Importantly, this subset can not be enlarged, and the respective error cannot be decreased, by considering deeper encoder/decoder networks. | https://openreview.net/pdf/a5374002f74f6bc2b38c0470b1886b02536c628f.pdf |
Generalized Decision Transformer for Offline Hindsight Information Matching | https://openreview.net/forum?id=CAjxVodl_v | https://openreview.net/forum?id=CAjxVodl_v | Hiroki Furuta,Yutaka Matsuo,Shixiang Shane Gu | ICLR 2022,Spotlight | How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for practical applications. Recent works have shown that using expressive policy function approximators and conditioning on future trajectory information -- such as future states in hindsight experience replay (HER) or returns-to-go in Decision Transformer (DT) -- enables efficient learning of multi-task policies, where at times online RL is fully replaced by offline behavioral cloning (BC), e.g. sequence modeling. We demonstrate that all these approaches are doing hindsight information matching (HIM) -- training policies that can output the rest of trajectory that matches some statistics of future state information. We present Generalized Decision Transformer (GDT) for solving any HIM problem, and show how different choices for the feature function and the anti-causal aggregator not only recover DT as a special case, but also lead to novel Categorical DT (CDT) and Bi-directional DT (BDT) for matching different statistics of the future. For evaluating CDT and BDT, we define offline multi-task state-marginal matching (SMM) and imitation learning (IL) as two generic HIM problems, propose a Wasserstein distance loss as a metric for both, and empirically study them on MuJoCo continuous control benchmarks. Categorical DT, which simply replaces anti-causal summation with anti-causal binning in DT, enables arguably the first effective offline multi-task SMM algorithm that generalizes well to unseen (and even synthetic) multi-modal reward or state-feature distributions. Bi-directional DT, which uses an anti-causal second transformer as the aggregator, can learn to model any statistics of the future and outperforms DT variants in offline multi-task IL, i.e. one-shot IL. Our generalized formulations from HIM and GDT greatly expand the role of powerful sequence modeling architectures in modern RL. | https://openreview.net/pdf/86d7058e78842b10462a9f0e0311ca3040adfe97.pdf |
Unifying Likelihood-free Inference with Black-box Optimization and Beyond | https://openreview.net/forum?id=1HxTO6CTkz | https://openreview.net/forum?id=1HxTO6CTkz | Dinghuai Zhang,Jie Fu,Yoshua Bengio,Aaron Courville | ICLR 2022,Spotlight | Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box optimization, under one probabilistic framework. In tandem, we provide a recipe for constructing various sequence design methods based on this framework. We show how previous optimization approaches can be "reinvented" in our framework, and further propose new probabilistic black-box optimization algorithms. Extensive experiments on sequence design application illustrate the benefits of the proposed methodology. | https://openreview.net/pdf/e2ec346ff6de5e9270bf7e826ba6ff87f1b8055b.pdf |
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting | https://openreview.net/forum?id=AJAR-JgNw__ | https://openreview.net/forum?id=AJAR-JgNw__ | Wei Fan,Shun Zheng,Xiaohan Yi,Wei Cao,Yanjie Fu,Jiang Bian,Tie-Yan Liu | ICLR 2022,Spotlight | Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting. In this paper, we introduce a deep expansion learning framework, DEPTS, for PTS forecasting. DEPTS starts with a decoupled formulation by introducing the periodic state as a hidden variable, which stimulates us to make two dedicated modules to tackle the aforementioned two challenges. First, we develop an expansion module on top of residual learning to perform a layer-by-layer expansion of those complicated dependencies. Second, we introduce a periodicity module with a parameterized periodic function that holds sufficient capacity to capture diversified periods. Moreover, our two customized modules also have certain interpretable capabilities, such as attributing the forecasts to either local momenta or global periodicity and characterizing certain core periodic properties, e.g., amplitudes and frequencies. Extensive experiments on both synthetic data and real-world data demonstrate the effectiveness of DEPTS on handling PTS. In most cases, DEPTS achieves significant improvements over the best baseline. Specifically, the error reduction can even reach up to 20% for a few cases. All codes for this paper are publicly available. | https://openreview.net/pdf/cd132957a26c075bcbe5f26a96995eea829b38e0.pdf |
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions | https://openreview.net/forum?id=tBtoZYKd9n | https://openreview.net/forum?id=tBtoZYKd9n | Leslie O'Bray,Max Horn,Bastian Rieck,Karsten Borgwardt | ICLR 2022,Spotlight | Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for such a comparison metric and provide an overview of the status quo of graph generative model comparison in use today, which predominantly relies on the maximum mean discrepancy (MMD). We perform a systematic evaluation of MMD in the context of graph generative model comparison, highlighting some of the challenges and pitfalls researchers inadvertently may encounter. After conducting a thorough analysis of the behaviour of MMD on synthetically-generated perturbed graphs as well as on recently-proposed graph generative models, we are able to provide a suitable procedure to mitigate these challenges and pitfalls. We aggregate our findings into a list of practical recommendations for researchers to use when evaluating graph generative models. | https://openreview.net/pdf/3af218144851b57d7d59c78ea79729bed4f8adba.pdf |
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions | https://openreview.net/forum?id=tV3N0DWMxCg | https://openreview.net/forum?id=tV3N0DWMxCg | Bertrand Charpentier,Oliver Borchert,Daniel Zügner,Simon Geisler,Stephan Günnemann | ICLR 2022,Spotlight | Uncertainty awareness is crucial to develop reliable machine learning models. In this work, we propose the Natural Posterior Network (NatPN) for fast and high-quality uncertainty estimation for any task where the target distribution belongs to the exponential family. Thus, NatPN finds application for both classification and general regression settings. Unlike many previous approaches, NatPN does not require out-of-distribution (OOD) data at training time. Instead, it leverages Normalizing Flows to fit a single density on a learned low-dimensional and task-dependent latent space. For any input sample, NatPN uses the predicted likelihood to perform a Bayesian update over the target distribution. Theoretically, NatPN assigns high uncertainty far away from training data. Empirically, our extensive experiments on calibration and OOD detection show that NatPN delivers highly competitive performance for classification, regression and count prediction tasks. | https://openreview.net/pdf/c3ffe01a3bb574ad88e06692cb426a681a7c0f54.pdf |
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards | https://openreview.net/forum?id=KxbhdyiPHE | https://openreview.net/forum?id=KxbhdyiPHE | Tim Franzmeyer,Mateusz Malinowski,Joao F. Henriques | ICLR 2022,Spotlight | Can artificial agents learn to assist others in achieving their goals without knowing what those goals are? Generic reinforcement learning agents could be trained to behave altruistically towards others by rewarding them for altruistic behaviour, i.e., rewarding them for benefiting other agents in a given situation. Such an approach assumes that other agents' goals are known so that the altruistic agent can cooperate in achieving those goals. However, explicit knowledge of other agents' goals is often difficult to acquire. In the case of human agents, their goals and preferences may be difficult to express fully; they might be ambiguous or even contradictory. Thus, it is beneficial to develop agents that do not depend on external supervision and learn altruistic behaviour in a task-agnostic manner. We propose to act altruistically towards other agents by giving them more choice and allowing them to achieve their goals better. Some concrete examples include opening a door for others or safeguarding them to pursue their objectives without interference. We formalize this concept and propose an altruistic agent that learns to increase the choices another agent has by preferring to maximize the number of states that the other agent can reach in its future. We evaluate our approach in three different multi-agent environments where another agent's success depends on altruistic behaviour. Finally, we show that our unsupervised agents can perform comparably to agents explicitly trained to work cooperatively, in some cases even outperforming them. | https://openreview.net/pdf/1af1674dc962e470709ff0dba09d61b75acd8daa.pdf |
Context-Aware Sparse Deep Coordination Graphs | https://openreview.net/forum?id=wQfgfb8VKTn | https://openreview.net/forum?id=wQfgfb8VKTn | Tonghan Wang,Liang Zeng,Weijun Dong,Qianlan Yang,Yang Yu,Chongjie Zhang | ICLR 2022,Spotlight | Learning sparse coordination graphs adaptive to the coordination dynamics among agents is a long-standing problem in cooperative multi-agent learning. This paper studies this problem and proposes a novel method using the variance of payoff functions to construct context-aware sparse coordination topologies. We theoretically consolidate our method by proving that the smaller the variance of payoff functions is, the less likely action selection will change after removing the corresponding edge. Moreover, we propose to learn action representations to effectively reduce the influence of payoff functions' estimation errors on graph construction. To empirically evaluate our method, we present the Multi-Agent COordination (MACO) benchmark by collecting classic coordination problems in the literature, increasing their difficulty, and classifying them into different types. We carry out a case study and experiments on the MACO and StarCraft II micromanagement benchmark to demonstrate the dynamics of sparse graph learning, the influence of graph sparseness, and the learning performance of our method. | https://openreview.net/pdf/a4e94260f9a234c7a8d59eb139b8f31b32a87673.pdf |
On the approximation properties of recurrent encoder-decoder architectures | https://openreview.net/forum?id=xDIvIqQ3DXD | https://openreview.net/forum?id=xDIvIqQ3DXD | Zhong Li,Haotian Jiang,Qianxiao Li | ICLR 2022,Spotlight | Encoder-decoder architectures have recently gained popularity in sequence to sequence modelling, featuring in state-of-the-art models such as transformers. However, a mathematical understanding of their working principles still remains limited. In this paper, we study the approximation properties of recurrent encoder-decoder architectures. Prior work established theoretical results for RNNs in the linear setting, where approximation capabilities can be related to smoothness and memory of target temporal relationships. Here, we uncover that the encoder and decoder together form a particular “temporal product structure” which determines the approximation efficiency. Moreover, the encoder-decoder architecture generalises RNNs with the capability to learn time-inhomogeneous relationships. Our results provide the theoretical understanding of approximation properties of the recurrent encoder-decoder architecture, which precisely characterises, in the considered setting, the types of temporal relationships that can be efficiently learned. | https://openreview.net/pdf/93c3702cdbb8429d512ae64ed57daf520f84137f.pdf |
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models | https://openreview.net/forum?id=Nfl-iXa-y7R | https://openreview.net/forum?id=Nfl-iXa-y7R | Beidi Chen,Tri Dao,Kaizhao Liang,Jiaming Yang,Zhao Song,Atri Rudra,Christopher Re | ICLR 2022,Spotlight | Overparameterized neural networks generalize well but are expensive to train. Ideally one would like to reduce their computational cost while retaining their generalization benefits. Sparse model training is a simple and promising approach to achieve this, but there remain challenges as existing methods struggle with accuracy loss, slow training runtime, or difficulty in sparsifying all model components. The core problem is that searching for a sparsity mask over a discrete set of sparse matrices is difficult and expensive. To address this, our main insight is to optimize over a continuous superset of sparse matrices with a fixed structure known as products of butterfly matrices. As butterfly matrices are not hardware efficient, we propose simple variants of butterfly (block and flat) to take advantage of modern hardware. Our method (Pixelated Butterfly) uses a simple fixed sparsity pattern based on flat block butterfly and low-rank matrices to sparsify most network layers (e.g., attention, MLP). We empirically validate that Pixelated Butterfly is $3\times$ faster than Butterfly and speeds up training to achieve favorable accuracy--efficiency tradeoffs. On the ImageNet classification and WikiText-103 language modeling tasks, our sparse models train up to 2.3$\times$ faster than the dense MLP-Mixer, Vision Transformer, and GPT-2 small with no drop in accuracy. | https://openreview.net/pdf/ee0e47a9502622a5bc9e044424d6f3217c00bdf4.pdf |
8-bit Optimizers via Block-wise Quantization | https://openreview.net/forum?id=shpkpVXzo3h | https://openreview.net/forum?id=shpkpVXzo3h | Tim Dettmers,Mike Lewis,Sam Shleifer,Luke Zettlemoyer | ICLR 2022,Spotlight | Stateful optimizers maintain gradient statistics over time, e.g., the exponentially smoothed sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can be used to accelerate optimization significantly, compared to plain stochastic gradient descent, but uses memory that might otherwise be allocated to model parameters, thereby limiting the maximum size of models trained in practice. In this paper, we develop the first optimizers that use 8-bit statistics while maintaining the performance levels of using 32-bit optimizer states. To overcome the resulting computational, quantization, and stability challenges, we develop block-wise dynamic quantization. Block-wise quantization divides input tensors into smaller blocks that are independently quantized. Each block is processed in parallel across cores, yielding faster optimization and high precision quantization. To maintain stability and performance, we combine block-wise quantization with two additional changes: (1) dynamic quantization, a form of non-linear optimization that is precise for both large and small magnitude values, and (2) a stable embedding layer to reduce gradient variance that comes from the highly non-uniform distribution of input tokens in language models. As a result, our 8-bit optimizers maintain 32-bit performance with a small fraction of the memory footprint on a range of tasks, including 1.5B parameter language modeling, GLUE finetuning, ImageNet classification, WMT'14 machine translation, MoCo v2 contrastive ImageNet pretraining+finetuning, and RoBERTa pretraining, without changes to the original optimizer hyperparameters. We open-source our 8-bit optimizers as a drop-in replacement that only requires a two-line code change. | https://openreview.net/pdf/eae16788bf15e102fb9f104d044c6dff582683f4.pdf |
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks | https://openreview.net/forum?id=yeP_zx9vqNm | https://openreview.net/forum?id=yeP_zx9vqNm | Anne Harrington,Arturo Deza | ICLR 2022,Spotlight | Recent work suggests that feature constraints in the training datasets of deep neural networks (DNNs) drive robustness to adversarial noise (Ilyas et al., 2019). The representations learned by such adversarially robust networks have also been shown to be more human perceptually-aligned than non-robust networks via image manipulations (Santurkar et al., 2019, Engstrom et al., 2019). Despite appearing closer to human visual perception, it is unclear if the constraints in robust DNN representations match biological constraints found in human vision. Human vision seems to rely on texture-based/summary statistic representations in the periphery, which have been shown to explain phenomena such as crowding (Balas et al., 2009) and performance on visual search tasks (Rosenholtz et al., 2012). To understand how adversarially robust optimizations/representations compare to human vision, we performed a psychophysics experiment using a metamer task similar to Freeman \& Simoncelli, 2011, Wallis et al., 2016 and Deza et al., 2019 where we evaluated how well human observers could distinguish between images synthesized to match adversarially robust representations compared to non-robust representations and a texture synthesis model of peripheral vision (Texforms a la Long et al., 2018). We found that the discriminability of robust representation and texture model images decreased to near chance performance as stimuli were presented farther in the periphery. Moreover, performance on robust and texture-model images showed similar trends within participants, while performance on non-robust representations changed minimally across the visual field. These results together suggest that (1) adversarially robust representations capture peripheral computation better than non-robust representations and (2) robust representations capture peripheral computation similar to current state-of-the-art texture peripheral vision models. More broadly, our findings support the idea that localized texture summary statistic representations may drive human invariance to adversarial perturbations and that the incorporation of such representations in DNNs could give rise to useful properties like adversarial robustness. | https://openreview.net/pdf/7f2e10fe0e775d6b9a7ac2d2d46206fcefd3f1ca.pdf |
Omni-Dimensional Dynamic Convolution | https://openreview.net/forum?id=DmpCfq6Mg39 | https://openreview.net/forum?id=DmpCfq6Mg39 | Chao Li,Aojun Zhou,Anbang Yao | ICLR 2022,Spotlight | Learning a single static convolutional kernel in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research in dynamic convolution shows that learning a linear combination of n convolutional kernels weighted with their input-dependent attentions can significantly improve the accuracy of light-weight CNNs, while maintaining efficient inference. However, we observe that existing works endow convolutional kernels with the dynamic property through one dimension (regarding the convolutional kernel number) of the kernel space, but the other three dimensions (regarding the spatial size, the input channel number and the output channel number for each convolutional kernel) are overlooked. Inspired by this, we present Omni-dimensional Dynamic Convolution (ODConv), a more generalized yet elegant dynamic convolution design, to advance this line of research. ODConv leverages a novel multi-dimensional attention mechanism with a parallel strategy to learn complementary attentions for convolutional kernels along all four dimensions of the kernel space at any convolutional layer. As a drop-in replacement of regular convolutions, ODConv can be plugged into many CNN architectures. Extensive experiments on the ImageNet and MS-COCO datasets show that ODConv brings solid accuracy boosts for various prevailing CNN backbones including both light-weight and large ones, e.g., 3.77%~5.71%|1.86%~3.72% absolute top-1 improvements to MobivleNetV2|ResNet family on the ImageNet dataset. Intriguingly, thanks to its improved feature learning ability, ODConv with even one single kernel can compete with or outperform existing dynamic convolution counterparts with multiple kernels, substantially reducing extra parameters. Furthermore, ODConv is also superior to other attention modules for modulating the output features or the convolutional weights. Code and models will be available at https://github.com/OSVAI/ODConv. | https://openreview.net/pdf/7b2dd41d0729d79f0f22fac00e8ac757b46ff5a9.pdf |
EViT: Expediting Vision Transformers via Token Reorganizations | https://openreview.net/forum?id=BjyvwnXXVn_ | https://openreview.net/forum?id=BjyvwnXXVn_ | Youwei Liang,Chongjian GE,Zhan Tong,Yibing Song,Jue Wang,Pengtao Xie | ICLR 2022,Spotlight | Vision Transformers (ViTs) take all the image patches as tokens and construct multi-head self-attention (MHSA) among them. Complete leverage of these image tokens brings redundant computations since not all the tokens are attentive in MHSA. Examples include that tokens containing semantically meaningless or distractive image backgrounds do not positively contribute to the ViT predictions. In this work, we propose to reorganize image tokens during the feed-forward process of ViT models, which is integrated into ViT during training. For each forward inference, we identify the attentive image tokens between MHSA and FFN (i.e., feed-forward network) modules, which is guided by the corresponding class token attention. Then, we reorganize image tokens by preserving attentive image tokens and fusing inattentive ones to expedite subsequent MHSA and FFN computations. To this end, our method EViT improves ViTs from two perspectives. First, under the same amount of input image tokens, our method reduces MHSA and FFN computation for efficient inference. For instance, the inference speed of DeiT-S is increased by 50% while its recognition accuracy is decreased by only 0.3% for ImageNet classification. Second, by maintaining the same computational cost, our method empowers ViTs to take more image tokens as input for recognition accuracy improvement, where the image tokens are from higher resolution images. An example is that we improve the recognition accuracy of DeiT-S by 1% for ImageNet classification at the same computational cost of a vanilla DeiT-S. Meanwhile, our method does not introduce more parameters to ViTs. Experiments on the standard benchmarks show the effectiveness of our method. The code is available at https://github.com/youweiliang/evit | https://openreview.net/pdf/feb0c5a2e1c1fc63509c2e528ca07aa95aea2d5e.pdf |
D-CODE: Discovering Closed-form ODEs from Observed Trajectories | https://openreview.net/forum?id=wENMvIsxNN | https://openreview.net/forum?id=wENMvIsxNN | Zhaozhi Qian,Krzysztof Kacprzyk,Mihaela van der Schaar | ICLR 2022,Spotlight | For centuries, scientists have manually designed closed-form ordinary differential equations (ODEs) to model dynamical systems. An automated tool to distill closed-form ODEs from observed trajectories would accelerate the modeling process. Traditionally, symbolic regression is used to uncover a closed-form prediction function $a=f(b)$ with label-feature pairs $(a_i, b_i)$ as training examples. However, an ODE models the time derivative $\dot{x}(t)$ of a dynamical system, e.g. $\dot{x}(t) = f(x(t),t)$, and the "label" $\dot{x}(t)$ is usually *not* observed. The existing ways to bridge this gap only perform well for a narrow range of settings with low measurement noise, frequent sampling, and non-chaotic dynamics. In this work, we propose the Discovery of Closed-form ODE framework (D-CODE), which advances symbolic regression beyond the paradigm of supervised learning. D-CODE leverages a novel objective function based on the variational formulation of ODEs to bypass the unobserved time derivative. For formal justification, we prove that this objective is a valid proxy for the estimation error of the true (but unknown) ODE. In the experiments, D-CODE successfully discovered the governing equations of a diverse range of dynamical systems under challenging measurement settings with high noise and infrequent sampling. | https://openreview.net/pdf/3a0bdc107d197bd18aa7299f8d8f198db2225d03.pdf |
Spanning Tree-based Graph Generation for Molecules | https://openreview.net/forum?id=w60btE_8T2m | https://openreview.net/forum?id=w60btE_8T2m | Sungsoo Ahn,Binghong Chen,Tianzhe Wang,Le Song | ICLR 2022,Spotlight | In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry. To this end, we propose a spanning tree-based graph generation (STGG) framework based on formulating molecular graph generation as a construction of a spanning tree and the residual edges. Such a formulation exploits the sparsity of molecular graphs and allows using compact tree-constructive operations to define the molecular graph connectivity. Based on the intermediate graph structure of the construction process, our framework can constrain its generation to molecular graphs that satisfy the chemical valence rules. We also newly design a Transformer architecture with tree-based relative positional encodings for realizing the tree construction procedure. Experiments on QM9, ZINC250k, and MOSES benchmarks verify the effectiveness of the proposed framework in metrics such as validity, Frechet ChemNet distance, and fragment similarity. We also demonstrate the usefulness of STGG in maximizing penalized LogP value of molecules. | https://openreview.net/pdf/dcb1134d836d26dc8ef7d83683aa9f5b35964eae.pdf |
Policy improvement by planning with Gumbel | https://openreview.net/forum?id=bERaNdoegnO | https://openreview.net/forum?id=bERaNdoegnO | Ivo Danihelka,Arthur Guez,Julian Schrittwieser,David Silver | ICLR 2022,Spotlight | AlphaZero is a powerful reinforcement learning algorithm based on approximate policy iteration and tree search. However, AlphaZero can fail to improve its policy network, if not visiting all actions at the root of a search tree. To address this issue, we propose a policy improvement algorithm based on sampling actions without replacement. Furthermore, we use the idea of policy improvement to replace the more heuristic mechanisms by which AlphaZero selects and uses actions, both at root nodes and at non-root nodes. Our new algorithms, Gumbel AlphaZero and Gumbel MuZero, respectively without and with model-learning, match the state of the art on Go, chess, and Atari, and significantly improve prior performance when planning with few simulations. | https://openreview.net/pdf/4f2c0c813d0fbe127329c69b1ba216fbcd95d52c.pdf |
Learning Optimal Conformal Classifiers | https://openreview.net/forum?id=t8O-4LKFVx | https://openreview.net/forum?id=t8O-4LKFVx | David Stutz,Krishnamurthy Dj Dvijotham,Ali Taylan Cemgil,Arnaud Doucet | ICLR 2022,Spotlight | Modern deep learning based classifiers show very high accuracy on test data but this does not provide sufficient guarantees for safe deployment, especially in high-stake AI applications such as medical diagnosis. Usually, predictions are obtained without a reliable uncertainty estimate or a formal guarantee. Conformal prediction (CP) addresses these issues by using the classifier's predictions, e.g., its probability estimates, to predict confidence sets containing the true class with a user-specified probability. However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets. Thus, this paper explores strategies to differentiate through CP during training with the goal of training model with the conformal wrapper end-to-end. In our approach, conformal training (ConfTr), we specifically "simulate" conformalization on mini-batches during training. Compared to standard training, ConfTr reduces the average confidence set size (inefficiency) of state-of-the-art CP methods applied after training. Moreover, it allows to "shape" the confidence sets predicted at test time, which is difficult for standard CP. On experiments with several datasets, we show ConfTr can influence how inefficiency is distributed across classes, or guide the composition of confidence sets in terms of the included classes, while retaining the guarantees offered by CP. | https://openreview.net/pdf/9fab4fe6695fabd7230072d001a43466d0e92499.pdf |
Multitask Prompted Training Enables Zero-Shot Task Generalization | https://openreview.net/forum?id=9Vrb9D0WI4 | https://openreview.net/forum?id=9Vrb9D0WI4 | Victor Sanh,Albert Webson,Colin Raffel,Stephen Bach,Lintang Sutawika,Zaid Alyafeai,Antoine Chaffin,Arnaud Stiegler,Arun Raja,Manan Dey,M Saiful Bari,Canwen Xu,Urmish Thakker,Shanya Sharma Sharma,Eliza Szczechla,Taewoon Kim,Gunjan Chhablani,Nihal Nayak,Debajyoti Datta,Jonathan Chang,Mike Tian-Jian Jiang,Han Wang,Matteo Manica,Sheng Shen,Zheng Xin Yong,Harshit Pandey,Rachel Bawden,Thomas Wang,Trishala Neeraj,Jos Rozen,Abheesht Sharma,Andrea Santilli,Thibault Fevry,Jason Alan Fries,Ryan Teehan,Teven Le Scao,Stella Biderman,Leo Gao,Thomas Wolf,Alexander M Rush | ICLR 2022,Spotlight | Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models’ pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several datasets, often outperforming models 16× its size. Further, our model attains strong performance on a subset of tasks from the BIG-Bench benchmark, outperforming models 6× its size. All trained models are available at https://github.com/bigscience-workshop/t-zero, and all prompts are available at https://github.com/bigscience-workshop/promptsource. | https://openreview.net/pdf/bec425b93713482f8e2de5d1d15b66ff95a47026.pdf |
Continuous-Time Meta-Learning with Forward Mode Differentiation | https://openreview.net/forum?id=57PipS27Km | https://openreview.net/forum?id=57PipS27Km | Tristan Deleu,David Kanaa,Leo Feng,Giancarlo Kerg,Yoshua Bengio,Guillaume Lajoie,Pierre-Luc Bacon | ICLR 2022,Spotlight | Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field. Specifically, representations of the inputs are meta-learned such that a task-specific linear classifier is obtained as a solution of an ordinary differential equation (ODE). Treating the learning process as an ODE offers the notable advantage that the length of the trajectory is now continuous, as opposed to a fixed and discrete number of gradient steps. As a consequence, we can optimize the amount of adaptation necessary to solve a new task using stochastic gradient descent, in addition to learning the initial conditions as is standard practice in gradient-based meta-learning. Importantly, in order to compute the exact meta-gradients required for the outer-loop updates, we devise an efficient algorithm based on forward mode differentiation, whose memory requirements do not scale with the length of the learning trajectory, thus allowing longer adaptation in constant memory. We provide analytical guarantees for the stability of COMLN, we show empirically its efficiency in terms of runtime and memory usage, and we illustrate its effectiveness on a range of few-shot image classification problems. | https://openreview.net/pdf/68b03565ea73b881a10643d2e81e4ace23821ef2.pdf |
On the relation between statistical learning and perceptual distances | https://openreview.net/forum?id=zXM0b4hi5_B | https://openreview.net/forum?id=zXM0b4hi5_B | Alexander Hepburn,Valero Laparra,Raul Santos-Rodriguez,Johannes Ballé,Jesus Malo | ICLR 2022,Spotlight | It has been demonstrated many times that the behavior of the human visual system is connected to the statistics of natural images. Since machine learning relies on the statistics of training data as well, the above connection has interesting implications when using perceptual distances (which mimic the behavior of the human visual system) as a loss function. In this paper, we aim to unravel the non-trivial relationships between the probability distribution of the data, perceptual distances, and unsupervised machine learning. To this end, we show that perceptual sensitivity is correlated with the probability of an image in its close neighborhood. We also explore the relation between distances induced by autoencoders and the probability distribution of the training data, as well as how these induced distances are correlated with human perception. Finally, we find perceptual distances do not always lead to noticeable gains in performance over Euclidean distance in common image processing tasks, except when data is scarce and the perceptual distance provides regularization. We propose this may be due to a double-counting effect of the image statistics, once in the perceptual distance and once in the training procedure. | https://openreview.net/pdf/12c717193deff83fed4cdbbc207d6c4ffebad63e.pdf |
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension | https://openreview.net/forum?id=vA7doMdgi75 | https://openreview.net/forum?id=vA7doMdgi75 | Paris Giampouras,Benjamin David Haeffele,Rene Vidal | ICLR 2022,Spotlight | Robust subspace recovery (RSR) is the problem of learning a subspace from sample data points corrupted by outliers. Dual Principal Component Pursuit (DPCP) is a robust subspace recovery method that aims to find a basis for the orthogonal complement of the subspace by minimizing the sum of the distances of the points to the subspaces subject to orthogonality constraints on the basis. Prior work has shown that DPCP can provably recover the correct subspace in the presence of outliers as long as the true dimension of the subspace is known. In this paper, we show that if the orthogonality constraints --adopted in previous DPCP formulations-- are relaxed and random initialization is used instead of spectral one, DPCP can provably recover a subspace of \emph{unknown dimension}. Specifically, we propose a very simple algorithm based on running multiple instances of a projected sub-gradient descent method (PSGM), with each problem instance seeking to find one vector in the null space of the subspace. We theoretically prove that under mild conditions this approach succeeds with high probability. In particular, we show that 1) all of the problem instances will converge to a vector in the nullspace of the subspace and 2) the ensemble of problem instance solutions will be sufficiently diverse to fully span the nullspace of the subspace thus also revealing its true unknown codimension. We provide empirical results that corroborate our theoretical results and showcase the remarkable implicit rank regularization behavior of the PSGM algorithm that allows us to perform RSR without knowing the subspace dimension | https://openreview.net/pdf/4590e3755b5109113b2a58fd038313d6a8b091ec.pdf |
On the Optimal Memorization Power of ReLU Neural Networks | https://openreview.net/forum?id=MkTPtnjeYTV | https://openreview.net/forum?id=MkTPtnjeYTV | Gal Vardi,Gilad Yehudai,Ohad Shamir | ICLR 2022,Spotlight | We study the memorization power of feedforward ReLU neural networks. We show that such networks can memorize any $N$ points that satisfy a mild separability assumption using $\tilde{O}\left(\sqrt{N}\right)$ parameters. Known VC-dimension upper bounds imply that memorizing $N$ samples requires $\Omega(\sqrt{N})$ parameters, and hence our construction is optimal up to logarithmic factors. We also give a generalized construction for networks with depth bounded by $1 \leq L \leq \sqrt{N}$, for memorizing $N$ samples using $\tilde{O}(N/L)$ parameters. This bound is also optimal up to logarithmic factors. Our construction uses weights with large bit complexity. We prove that having such a large bit complexity is both necessary and sufficient for memorization with a sub-linear number of parameters. | https://openreview.net/pdf/6278a5d471f74abf6613e3926eac6ce535669473.pdf |
Programmatic Reinforcement Learning without Oracles | https://openreview.net/forum?id=6Tk2noBdvxt | https://openreview.net/forum?id=6Tk2noBdvxt | Wenjie Qiu,He Zhu | ICLR 2022,Spotlight | Deep reinforcement learning (RL) has led to encouraging successes in many challenging control tasks. However, a deep RL model lacks interpretability due to the difficulty of identifying how the model's control logic relates to its network structure. Programmatic policies structured in more interpretable representations emerge as a promising solution. Yet two shortcomings remain: First, synthesizing programmatic policies requires optimizing over the discrete and non-differentiable search space of program architectures. Previous works are suboptimal because they only enumerate program architectures greedily guided by a pretrained RL oracle. Second, these works do not exploit compositionality, an important programming concept, to reuse and compose primitive functions to form a complex function for new tasks. Our first contribution is a programmatically interpretable RL framework that conducts program architecture search on top of a continuous relaxation of the architecture space defined by programming language grammar rules. Our algorithm allows policy architectures to be learned with policy parameters via bilevel optimization using efficient policy-gradient methods, and thus does not require a pretrained oracle. Our second contribution is improving programmatic policies to support compositionality by integrating primitive functions learned to grasp task-agnostic skills as a composite program to solve novel RL problems. Experiment results demonstrate that our algorithm excels in discovering optimal programmatic policies that are highly interpretable. The code of this work is available at https://github.com/RU-Automated-Reasoning-Group/pi-PRL. | https://openreview.net/pdf/92dbdb48fe9a64f9e46e509762a9443b84450f68.pdf |
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations | https://openreview.net/forum?id=LtKcMgGOeLt | https://openreview.net/forum?id=LtKcMgGOeLt | Xiangning Chen,Cho-Jui Hsieh,Boqing Gong | ICLR 2022,Spotlight | Vision Transformers (ViTs) and MLPs signal further efforts on replacing hand-wired features or inductive biases with general-purpose neural architectures. Existing works empower the models by massive data, such as large-scale pre-training and/or repeated strong data augmentations, and still report optimization-related problems (e.g., sensitivity to initialization and learning rates). Hence, this paper investigates ViTs and MLP-Mixers from the lens of loss geometry, intending to improve the models' data efficiency at training and generalization at inference. Visualization and Hessian reveal extremely sharp local minima of converged models. By promoting smoothness with a recently proposed sharpness-aware optimizer, we substantially improve the accuracy and robustness of ViTs and MLP-Mixers on various tasks spanning supervised, adversarial, contrastive, and transfer learning (e.g., +5.3\% and +11.0\% top-1 accuracy on ImageNet for ViT-B/16 and Mixer-B/16, respectively, with the simple Inception-style preprocessing). We show that the improved smoothness attributes to sparser active neurons in the first few layers. The resultant ViTs outperform ResNets of similar size and throughput when trained from scratch on ImageNet without large-scale pre-training or strong data augmentations. Model checkpoints are available at \url{https://github.com/google-research/vision_transformer}. | https://openreview.net/pdf/97b8505eb7034c4bfaee9c7d480a9f605be6fea8.pdf |
Online Hyperparameter Meta-Learning with Hypergradient Distillation | https://openreview.net/forum?id=01AMRlen9wJ | https://openreview.net/forum?id=01AMRlen9wJ | Hae Beom Lee,Hayeon Lee,JaeWoong Shin,Eunho Yang,Timothy Hospedales,Sung Ju Hwang | ICLR 2022,Spotlight | Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters. Although such hyperparameters can be optimized using the existing gradient-based hyperparameter optimization (HO) methods, they suffer from the following issues. Unrolled differentiation methods do not scale well to high-dimensional hyperparameters or horizon length, Implicit Function Theorem (IFT) based methods are restrictive for online optimization, and short horizon approximations suffer from short horizon bias. In this work, we propose a novel HO method that can overcome these limitations, by approximating the second-order term with knowledge distillation. Specifically, we parameterize a single Jacobian-vector product (JVP) for each HO step and minimize the distance from the true second-order term. Our method allows online optimization and also is scalable to the hyperparameter dimension and the horizon length. We demonstrate the effectiveness of our method on three different meta-learning methods and two benchmark datasets. | https://openreview.net/pdf/e1a0b8a3fc38e7d1b03adaf5cd0f110f66568bab.pdf |
Tighter Sparse Approximation Bounds for ReLU Neural Networks | https://openreview.net/forum?id=LBvk4QWIUpm | https://openreview.net/forum?id=LBvk4QWIUpm | Carles Domingo-Enrich,Youssef Mroueh | ICLR 2022,Spotlight | A well-known line of work (Barron, 1993; Breiman, 1993; Klusowski & Barron, 2018) provides bounds on the width $n$ of a ReLU two-layer neural network needed to approximate a function $f$ over the ball $\mathcal{B}_R(\mathbb{R}^d)$ up to error $\epsilon$, when the Fourier based quantity $C_f = \int_{\mathbb{R}^d} \|\xi\|^2 |\hat{f}(\xi)| \ d\xi$ is finite. More recently Ongie et al. (2019) used the Radon transform as a tool for analysis of infinite-width ReLU two-layer networks. In particular, they introduce the concept of Radon-based $\mathcal{R}$-norms and show that a function defined on $\mathbb{R}^d$ can be represented as an infinite-width two-layer neural network if and only if its $\mathcal{R}$-norm is finite. In this work, we extend the framework of Ongie et al. (2019) and define similar Radon-based semi-norms ($\mathcal{R}, \mathcal{U}$-norms) such that a function admits an infinite-width neural network representation on a bounded open set $\mathcal{U} \subseteq \mathbb{R}^d$ when its $\mathcal{R}, \mathcal{U}$-norm is finite. Building on this, we derive sparse (finite-width) neural network approximation bounds that refine those of Breiman (1993); Klusowski & Barron (2018). Finally, we show that infinite-width neural network representations on bounded open sets are not unique and study their structure, providing a functional view of mode connectivity. | https://openreview.net/pdf/26cbfd341c2fcbea04f1b531de54a26548f1ec2d.pdf |
Long Expressive Memory for Sequence Modeling | https://openreview.net/forum?id=vwj6aUeocyf | https://openreview.net/forum?id=vwj6aUeocyf | T. Konstantin Rusch,Siddhartha Mishra,N. Benjamin Erichson,Michael W. Mahoney | ICLR 2022,Spotlight | We propose a novel method called Long Expressive Memory (LEM) for learning long-term sequential dependencies. LEM is gradient-based, it can efficiently process sequential tasks with very long-term dependencies, and it is sufficiently expressive to be able to learn complicated input-output maps. To derive LEM, we consider a system of multiscale ordinary differential equations, as well as a suitable time-discretization of this system. For LEM, we derive rigorous bounds to show the mitigation of the exploding and vanishing gradients problem, a well-known challenge for gradient-based recurrent sequential learning methods. We also prove that LEM can approximate a large class of dynamical systems to high accuracy. Our empirical results, ranging from image and time-series classification through dynamical systems prediction to speech recognition and language modeling, demonstrate that LEM outperforms state-of-the-art recurrent neural networks, gated recurrent units, and long short-term memory models. | https://openreview.net/pdf/af3e85f49d797e1d183908208759b1776c72eb5d.pdf |
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy | https://openreview.net/forum?id=LzQQ89U1qm_ | https://openreview.net/forum?id=LzQQ89U1qm_ | Jiehui Xu,Haixu Wu,Jianmin Wang,Mingsheng Long | ICLR 2022,Spotlight | Unsupervised detection of anomaly points in time series is a challenging problem, which requires the model to derive a distinguishable criterion. Previous methods tackle the problem mainly through learning pointwise representation or pairwise association, however, neither is sufficient to reason about the intricate dynamics. Recently, Transformers have shown great power in unified modeling of pointwise representation and pairwise association, and we find that the self-attention weight distribution of each time point can embody rich association with the whole series. Our key observation is that due to the rarity of anomalies, it is extremely difficult to build nontrivial associations from abnormal points to the whole series, thereby, the anomalies' associations shall mainly concentrate on their adjacent time points. This adjacent-concentration bias implies an association-based criterion inherently distinguishable between normal and abnormal points, which we highlight through the Association Discrepancy. Technically, we propose the Anomaly Transformer with a new Anomaly-Attention mechanism to compute the association discrepancy. A minimax strategy is devised to amplify the normal-abnormal distinguishability of the association discrepancy. The Anomaly Transformer achieves state-of-the-art results on six unsupervised time series anomaly detection benchmarks of three applications: service monitoring, space & earth exploration, and water treatment. | https://openreview.net/pdf/b3974b079de39a5b7e379db64e3fe6b27d3bc07f.pdf |
Progressive Distillation for Fast Sampling of Diffusion Models | https://openreview.net/forum?id=TIdIXIpzhoI | https://openreview.net/forum?id=TIdIXIpzhoI | Tim Salimans,Jonathan Ho | ICLR 2022,Spotlight | Diffusion models have recently shown great promise for generative modeling, outperforming GANs on perceptual quality and autoregressive models at density estimation. A remaining downside is their slow sampling time: generating high quality samples takes many hundreds or thousands of model evaluations. Here we make two contributions to help eliminate this downside: First, we present new parameterizations of diffusion models that provide increased stability when using few sampling steps, compared to models in the literature. Second, we present a method to distill a trained deterministic diffusion sampler, using many steps, into a new diffusion model that takes half as many sampling steps. We then keep progressively applying this distillation procedure to our model, halving the number of required sampling steps each time. On standard image generation benchmarks like CIFAR-10, ImageNet, and LSUN, we start out with (near) state-of-the-art samplers taking 1024 or 8192 steps, and are able to distill down to models taking as little as 4 steps without losing much perceptual quality; achieving, for example, a FID of 3.0 on CIFAR-10 in 4 steps. Finally, we show that the full progressive distillation procedure does not take more time than it takes to train the original model, thus representing an efficient solution for generative modeling using diffusion at both train and test time. | https://openreview.net/pdf/3b30857a628099896b6123e85d6cf04c59abe77b.pdf |
A General Analysis of Example-Selection for Stochastic Gradient Descent | https://openreview.net/forum?id=7gWSJrP3opB | https://openreview.net/forum?id=7gWSJrP3opB | Yucheng Lu,Si Yi Meng,Christopher De Sa | ICLR 2022,Spotlight | Training example order in SGD has long been known to affect convergence rate. Recent results show that accelerated rates are possible in a variety of cases for permutation-based sample orders, in which each example from the training set is used once before any example is reused. In this paper, we develop a broad condition on the sequence of examples used by SGD that is sufficient to prove tight convergence rates in both strongly convex and non-convex settings. We show that our approach suffices to recover, and in some cases improve upon, previous state-of-the-art analyses for four known example-selection schemes: (1) shuffle once, (2) random reshuffling, (3) random reshuffling with data echoing, and (4) Markov Chain Gradient Descent. Motivated by our theory, we propose two new example-selection approaches. First, using quasi-Monte-Carlo methods, we achieve unprecedented accelerated convergence rates for learning with data augmentation. Second, we greedily choose a fixed scan-order to minimize the metric used in our condition and show that we can obtain more accurate solutions from the same number of epochs of SGD. We conclude by empirically demonstrating the utility of our approach for both convex linear-model and deep learning tasks. Our code is available at: https://github.com/EugeneLYC/qmc-ordering. | https://openreview.net/pdf/f12e4d11665b4464f75c539a335e09880c3e3472.pdf |
Assessing Generalization of SGD via Disagreement | https://openreview.net/forum?id=WvOGCEAQhxl | https://openreview.net/forum?id=WvOGCEAQhxl | Yiding Jiang,Vaishnavh Nagarajan,Christina Baek,J Zico Kolter | ICLR 2022,Spotlight | We empirically show that the test error of deep networks can be estimated by training the same architecture on the same training set but with two different runs of Stochastic Gradient Descent (SGD), and then measuring the disagreement rate between the two networks on unlabeled test data. This builds on -- and is a stronger version of -- the observation in Nakkiran&Bansal 20, which requires the runs to be on separate training sets. We further theoretically show that this peculiar phenomenon arises from the well-calibrated nature of ensembles of SGD-trained models. This finding not only provides a simple empirical measure to directly predict the test error using unlabeled test data, but also establishes a new conceptual connection between generalization and calibration. | https://openreview.net/pdf/c7e974d96a71d3095746891b113427e16a551bb3.pdf |
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning | https://openreview.net/forum?id=YZHES8wIdE | https://openreview.net/forum?id=YZHES8wIdE | Haichao Zhang,Wei Xu,Haonan Yu | ICLR 2022,Spotlight | Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return. While flexible, it faces difficulties arising from the inefficient exploration due to its single step nature. In this work, we present Generative Planning method (GPM), which can generate actions not only for the current step, but also for a number of future steps (thus termed as generative planning). This brings several benefits to GPM. Firstly, since GPM is trained by maximizing value, the plans generated from it can be regarded as intentional action sequences for reaching high value regions. GPM can therefore leverage its generated multi-step plans for temporally coordinated exploration towards high value regions, which is potentially more effective than a sequence of actions generated by perturbing each action at single step level, whose consistent movement decays exponentially with the number of exploration steps. Secondly, starting from a crude initial plan generator, GPM can refine it to be adaptive to the task, which, in return, benefits future explorations. This is potentially more effective than commonly used action-repeat strategy, which is non-adaptive in its form of plans. Additionally, since the multi-step plan can be interpreted as the intent of the agent from now to a span of time period into the future, it offers a more informative and intuitive signal for interpretation. Experiments are conducted on several benchmark environments and the results demonstrated its effectiveness compared with several baseline methods. | https://openreview.net/pdf/0e68ff1fa269567c6c6101685f2f721afcc5d0aa.pdf |
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning | https://openreview.net/forum?id=Y4cs1Z3HnqL | https://openreview.net/forum?id=Y4cs1Z3HnqL | Chenjia Bai,Lingxiao Wang,Zhuoran Yang,Zhi-Hong Deng,Animesh Garg,Peng Liu,Zhaoran Wang | ICLR 2022,Spotlight | Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problem by penalizing the Q-values of OOD actions or constraining the trained policy to be close to the behavior policy. Nevertheless, such methods typically prevent the generalization of value functions beyond the offline data and also lack precise characterization of OOD data. In this paper, we propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints. Specifically, PBRL conducts uncertainty quantification via the disagreement of bootstrapped Q-functions, and performs pessimistic updates by penalizing the value function based on the estimated uncertainty. To tackle the extrapolating error, we further propose a novel OOD sampling method. We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL. Extensive experiments on D4RL benchmark show that PBRL has better performance compared to the state-of-the-art algorithms. | https://openreview.net/pdf/cbca2d6ef1966129d7e95e65cae5bec0dc19f0ea.pdf |
Equivariant Subgraph Aggregation Networks | https://openreview.net/forum?id=dFbKQaRk15w | https://openreview.net/forum?id=dFbKQaRk15w | Beatrice Bevilacqua,Fabrizio Frasca,Derek Lim,Balasubramaniam Srinivasan,Chen Cai,Gopinath Balamurugan,Michael M. Bronstein,Haggai Maron | ICLR 2022,Spotlight | Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their expressive power. This paper proposes a novel framework called Equivariant Subgraph Aggregation Networks (ESAN) to address this issue. Our main observation is that while two graphs may not be distinguishable by an MPNN, they often contain distinguishable subgraphs. Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture. We develop novel variants of the 1-dimensional Weisfeiler-Leman (1-WL) test for graph isomorphism, and prove lower bounds on the expressiveness of ESAN in terms of these new WL variants. We further prove that our approach increases the expressive power of both MPNNs and more expressive architectures. Moreover, we provide theoretical results that describe how design choices such as the subgraph selection policy and equivariant neural architecture affect our architecture's expressive power. To deal with the increased computational cost, we propose a subgraph sampling scheme, which can be viewed as a stochastic version of our framework. A comprehensive set of experiments on real and synthetic datasets demonstrates that our framework improves the expressive power and overall performance of popular GNN architectures. | https://openreview.net/pdf/a9272500150ccf0f8fafbd6cb0a26e71c003663f.pdf |
How Do Vision Transformers Work? | https://openreview.net/forum?id=D78Go4hVcxO | https://openreview.net/forum?id=D78Go4hVcxO | Namuk Park,Songkuk Kim | ICLR 2022,Spotlight | The success of multi-head self-attentions (MSAs) for computer vision is now indisputable. However, little is known about how MSAs work. We present fundamental explanations to help better understand the nature of MSAs. In particular, we demonstrate the following properties of MSAs and Vision Transformers (ViTs): (1) MSAs improve not only accuracy but also generalization by flattening the loss landscapes. Such improvement is primarily attributable to their data specificity, not long-range dependency. On the other hand, ViTs suffer from non-convex losses. Large datasets and loss landscape smoothing methods alleviate this problem; (2) MSAs and Convs exhibit opposite behaviors. For example, MSAs are low-pass filters, but Convs are high-pass filters. Therefore, MSAs and Convs are complementary; (3) Multi-stage neural networks behave like a series connection of small individual models. In addition, MSAs at the end of a stage play a key role in prediction. Based on these insights, we propose AlterNet, a model in which Conv blocks at the end of a stage are replaced with MSA blocks. AlterNet outperforms CNNs not only in large data regimes but also in small data regimes. The code is available at https://github.com/xxxnell/how-do-vits-work. | https://openreview.net/pdf/e293cbd49c33a4924e5b45932a342361dd9845cf.pdf |
Variational methods for simulation-based inference | https://openreview.net/forum?id=kZ0UYdhqkNY | https://openreview.net/forum?id=kZ0UYdhqkNY | Manuel Glöckler,Michael Deistler,Jakob H. Macke | ICLR 2022,Spotlight | We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods. SNVI combines likelihood-estimation (or likelihood-ratio-estimation) with variational inference to achieve a scalable simulation-based inference approach. SNVI maintains the flexibility of likelihood(-ratio) estimation to allow arbitrary proposals for simulations, while simultaneously providing a functional estimate of the posterior distribution without requiring MCMC sampling. We present several variants of SNVI and demonstrate that they are substantially more computationally efficient than previous algorithms, without loss of accuracy on benchmark tasks. We apply SNVI to a neuroscience model of the pyloric network in the crab and demonstrate that it can infer the posterior distribution with one order of magnitude fewer simulations than previously reported. SNVI vastly reduces the computational cost of simulation-based inference while maintaining accuracy and flexibility, making it possible to tackle problems that were previously inaccessible. | https://openreview.net/pdf/7f95763dfe526e3086de5dd3efa1b9b647be0253.pdf |
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs | https://openreview.net/forum?id=JprM0p-q0Co | https://openreview.net/forum?id=JprM0p-q0Co | Zhisheng Xiao,Karsten Kreis,Arash Vahdat | ICLR 2022,Spotlight | A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing three key requirements including: high sample quality, mode coverage, and fast sampling. We call the challenge imposed by these requirements the generative learning trilemma, as the existing models often trade some of them for others. Particularly, denoising diffusion models have shown impressive sample quality and diversity, but their expensive sampling does not yet allow them to be applied in many real-world applications. In this paper, we argue that slow sampling in these models is fundamentally attributed to the Gaussian assumption in the denoising step which is justified only for small step sizes. To enable denoising with large steps, and hence, to reduce the total number of denoising steps, we propose to model the denoising distribution using a complex multimodal distribution. We introduce denoising diffusion generative adversarial networks (denoising diffusion GANs) that model each denoising step using a multimodal conditional GAN. Through extensive evaluations, we show that denoising diffusion GANs obtain sample quality and diversity competitive with original diffusion models while being 2000$\times$ faster on the CIFAR-10 dataset. Compared to traditional GANs, our model exhibits better mode coverage and sample diversity. To the best of our knowledge, denoising diffusion GAN is the first model that reduces sampling cost in diffusion models to an extent that allows them to be applied to real-world applications inexpensively. | https://openreview.net/pdf/6570cfc5a990e77af23d8a4c6b934ac249ba4426.pdf |
Imbedding Deep Neural Networks | https://openreview.net/forum?id=yKIAXjkJc2F | https://openreview.net/forum?id=yKIAXjkJc2F | Andrew Corbett,Dmitry Kangin | ICLR 2022,Spotlight | Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem. We propose a new approach which explicates the network's `depth' as a fundamental variable, thus reducing the problem to a system of forward-facing initial value problems. This new method is based on the principal of `Invariant Imbedding' for which we prove a general solution, applicable to all non-linear, vector-valued optimal control problems with both running and terminal loss.
Our new architectures provide a tangible tool for inspecting the theoretical--and to a great extent unexplained--properties of network depth. They also constitute a resource of discrete implementations of Neural ODEs comparable to classes of imbedded residual neural networks. Through a series of experiments, we show the competitive performance of the proposed architectures for supervised learning and time series prediction. | https://openreview.net/pdf/2696810601b722fa25baf9dd6ed1280d43c1c474.pdf |
Understanding and Preventing Capacity Loss in Reinforcement Learning | https://openreview.net/forum?id=ZkC8wKoLbQ7 | https://openreview.net/forum?id=ZkC8wKoLbQ7 | Clare Lyle,Mark Rowland,Will Dabney | ICLR 2022,Spotlight | The reinforcement learning (RL) problem is rife with sources of non-stationarity that can destabilize or inhibit learning progress.
We identify a key mechanism by which this occurs in agents using neural networks as function approximators: \textit{capacity loss}, whereby networks trained to predict a sequence of target values lose their ability to quickly fit new functions over time.
We demonstrate that capacity loss occurs in a broad range of RL agents and environments, and is particularly damaging to learning progress in sparse-reward tasks. We then present a simple regularizer, Initial Feature Regularization (InFeR), that mitigates this phenomenon by regressing a subspace of features towards its value at initialization, improving performance over a state-of-the-art model-free algorithm in the Atari 2600 suite. Finally, we study how this regularization affects different notions of capacity and evaluate other mechanisms by which it may improve performance. | https://openreview.net/pdf/4d5e54a26ae7558a8d6efeb0bcc7d5f6844aba2a.pdf |
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration | https://openreview.net/forum?id=1JDiK_TbV4S | https://openreview.net/forum?id=1JDiK_TbV4S | Cian Eastwood,Ian Mason,Chris Williams,Bernhard Schölkopf | ICLR 2022,Spotlight | Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain. We address these issues for a particularly pervasive type of domain shift called measurement shift which can be resolved by restoring the source features rather than extracting new ones. In particular, we propose Feature Restoration (FR) wherein we: (i) store a lightweight and flexible approximation of the feature distribution under the source data; and (ii) adapt the feature-extractor such that the approximate feature distribution under the target data realigns with that saved on the source. We additionally propose a bottom-up training scheme which boosts performance, which we call Bottom-Up Feature Restoration (BUFR). On real and synthetic data, we demonstrate that BUFR outperforms existing SFDA methods in terms of accuracy, calibration, and data efficiency, while being less reliant on the performance of the source model in the target domain.
| https://openreview.net/pdf/aeb9d0c4c2be949d33ea83ddad8547e536405144.pdf |
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design | https://openreview.net/forum?id=lnEaqbTJIRz | https://openreview.net/forum?id=lnEaqbTJIRz | Yoav Levine,Noam Wies,Daniel Jannai,Dan Navon,Yedid Hoshen,Amnon Shashua | ICLR 2022,Spotlight | Pretraining Neural Language Models (NLMs) over a large corpus involves chunking the text into training examples, which are contiguous text segments of sizes processable by the neural architecture. We highlight a bias introduced by this common practice: we prove that the pretrained NLM can model much stronger dependencies between text segments that appeared in the same training example, than it can between text segments that appeared in different training examples. This intuitive result has a twofold role. First, it formalizes the motivation behind a broad line of recent successful NLM training heuristics, proposed for the pretraining and fine-tuning stages, which do not necessarily appear related at first glance. Second, our result clearly indicates further improvements to be made in NLM pretraining for the benefit of Natural Language Understanding tasks. As an example, we propose ``kNN-Pretraining": we show that including semantically related non-neighboring sentences in the same pretraining example yields improved sentence representations and open domain question answering abilities. This theoretically motivated degree of freedom for pretraining example design indicates new training schemes for self-improving representations. | https://openreview.net/pdf/da15c2bd56f71f3d484f0da8f8b25aea19884b0a.pdf |
Emergent Communication at Scale | https://openreview.net/forum?id=AUGBfDIV9rL | https://openreview.net/forum?id=AUGBfDIV9rL | Rahma Chaabouni,Florian Strub,Florent Altché,Eugene Tarassov,Corentin Tallec,Elnaz Davoodi,Kory Wallace Mathewson,Olivier Tieleman,Angeliki Lazaridou,Bilal Piot | ICLR 2022,Spotlight | Emergent communication aims for a better understanding of human language evolution and building more efficient representations. We posit that reaching these goals will require scaling up, in contrast to a significant amount of literature that focuses on setting up small-scale problems to tease out desired properties of the emergent languages. We focus on three independent aspects to scale up, namely the dataset, task complexity, and population size. We provide a first set of results for large populations solving complex tasks on realistic large-scale datasets, as well as an easy-to-use codebase to enable further experimentation. In more complex tasks and datasets, we find that RL training can become unstable, but responds well to established stabilization techniques.
We also identify the need for a different metric than topographic similarity, which does not correlate with the generalization performances when working with natural images. In this context, we probe ease-of-learnability and transfer methods to assess emergent languages. Finally, we observe that larger populations do not induce robust emergent protocols with high generalization performance, leading us to explore different ways to leverage population, through voting and imitation learning. | https://openreview.net/pdf/89135b1aa6a72139d3332a71501a3d5d792156a8.pdf |
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings | https://openreview.net/forum?id=vds4SNooOe | https://openreview.net/forum?id=vds4SNooOe | Jingchao Ni,Wei Cheng,Zhengzhang Chen,Takayoshi Asakura,Tomoya Soma,Sho Kato,Haifeng Chen | ICLR 2022,Spotlight | Learning fine-grained embeddings is essential for extending the generalizability of models pre-trained on "coarse" labels (e.g., animals). It is crucial to fields for which fine-grained labeling (e.g., breeds of animals) is expensive, but fine-grained prediction is desirable, such as medicine. The dilemma necessitates adaptation of a "coarsely" pre-trained model to new tasks with a few "finer-grained" training labels. However, coarsely supervised pre-training tends to suppress intra-class variation, which is vital for cross-granularity adaptation. In this paper, we develop a training framework underlain by a novel superclass-conditional Gaussian mixture model (SCGM). SCGM imitates the generative process of samples from hierarchies of classes through latent variable modeling of the fine-grained subclasses. The framework is agnostic to the encoders and only adds a few distribution related parameters, thus is efficient, and flexible to different domains. The model parameters are learned end-to-end by maximum-likelihood estimation via a principled Expectation-Maximization algorithm. Extensive experiments on benchmark datasets and a real-life medical dataset indicate the effectiveness of our method. | https://openreview.net/pdf/6eed00839e8f02de0ecba5b42ac5ce892def5ac0.pdf |
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models | https://openreview.net/forum?id=-ApAkox5mp | https://openreview.net/forum?id=-ApAkox5mp | Zaccharie Ramzi,Florian Mannel,Shaojie Bai,Jean-Luc Starck,Philippe Ciuciu,Thomas Moreau | ICLR 2022,Spotlight | In recent years, implicit deep learning has emerged as a method to increase the depth of deep neural networks. While their training is memory-efficient, they are still significantly slower to train than their explicit counterparts. In Deep Equilibrium Models~(DEQs), the training is performed as a bi-level problem, and its computational complexity is partially driven by the iterative inversion of a huge Jacobian matrix. In this paper, we propose a novel strategy to tackle this computational bottleneck from which many bi-level problems suffer. The main idea is to use the quasi-Newton matrices from the forward pass to efficiently approximate the inverse Jacobian matrix in the direction needed for the gradient computation. We provide a theorem that motivates using our method with the original forward algorithms. In addition, by modifying these forward algorithms, we further provide theoretical guarantees that our method asymptotically estimates the true implicit gradient. We empirically study this approach in many settings, ranging from hyperparameter optimization to large Multiscale DEQs applied to CIFAR and ImageNet. We show that it reduces the computational cost of the backward pass by up to two orders of magnitude. All this is achieved while retaining the excellent performance of the original models in hyperparameter optimization and on CIFAR, and giving encouraging and competitive results on ImageNet. | https://openreview.net/pdf/d20ddecab60635b82dae5e9ac637ccb24c8038fc.pdf |
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality | https://openreview.net/forum?id=ccWaPGl9Hq | https://openreview.net/forum?id=ccWaPGl9Hq | Jiawei Huang,Jinglin Chen,Li Zhao,Tao Qin,Nan Jiang,Tie-Yan Liu | ICLR 2022,Spotlight | Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal theoretical formulation for the problem. In this paper, we propose such a formulation for deployment-efficient RL (DE-RL) from an ''optimization with constraints'' perspective: we are interested in exploring an MDP and obtaining a near-optimal policy within minimal \emph{deployment complexity}, whereas in each deployment the policy can sample a large batch of data. Using finite-horizon linear MDPs as a concrete structural model, we reveal the fundamental limit in achieving deployment efficiency by establishing information-theoretic lower bounds, and provide algorithms that achieve the optimal deployment efficiency. Moreover, our formulation for DE-RL is flexible and can serve as a building block for other practically relevant settings; we give ''Safe DE-RL'' and ''Sample-Efficient DE-RL'' as two examples, which may be worth future investigation. | https://openreview.net/pdf/a0405b77400aed6d75d6c20a20d9a5335d22d314.pdf |
Task Relatedness-Based Generalization Bounds for Meta Learning | https://openreview.net/forum?id=A3HHaEdqAJL | https://openreview.net/forum?id=A3HHaEdqAJL | Jiechao Guan,Zhiwu Lu | ICLR 2022,Spotlight | Supposing the $n$ training tasks and the new task are sampled from the same environment, traditional meta learning theory derives an error bound on the expected loss over the new task in terms of the empirical training loss, uniformly over the set of all hypothesis spaces. However, there is still little research on how the relatedness of these tasks can affect the full utilization of all $mn$ training data (with $m$ examples per task). In this paper, we propose to address this problem by defining a new notion of task relatedness according to the existence of the bijective transformation between two tasks. A novel generalization bound of $\mathcal{O}(\frac{1}{\sqrt{mn}})$ for meta learning is thus derived by exploiting the proposed task relatedness. Moreover, when investigating a special branch of meta learning that involves representation learning with deep neural networks, we establish spectrally-normalized bounds for both classification and regression problems. Finally, we demonstrate that the relatedness requirement between two tasks is satisfied when the sample space possesses the completeness and separability properties, validating the rationality and applicability of our proposed task-relatedness measure. | https://openreview.net/pdf/ebbc100be110ad3e6a5f5491100b967847d22082.pdf |
IntSGD: Adaptive Floatless Compression of Stochastic Gradients | https://openreview.net/forum?id=pFyXqxChZc | https://openreview.net/forum?id=pFyXqxChZc | Konstantin Mishchenko,Bokun Wang,Dmitry Kovalev,Peter Richtárik | ICLR 2022,Spotlight | We propose a family of adaptive integer compression operators for distributed Stochastic Gradient Descent (SGD) that do not communicate a single float. This is achieved by multiplying floating-point vectors with a number known to every device and then rounding to integers. In contrast to the prior work on integer compression for SwitchML by (Sapio et al., 2021), our IntSGD method is provably convergent and computationally cheaper as it estimates the scaling of vectors adaptively. Our theory shows that the iteration complexity of IntSGD matches that of SGD up to constant factors for both convex and non-convex, smooth and non-smooth functions, with and without overparameterization. Moreover, our algorithm can also be tailored for the popular all-reduce primitive and shows promising empirical performance. | https://openreview.net/pdf/fa05928174931d3f8e117dbba3da063cb29acbd2.pdf |
PAC-Bayes Information Bottleneck | https://openreview.net/forum?id=iLHOIDsPv1P | https://openreview.net/forum?id=iLHOIDsPv1P | Zifeng Wang,Shao-Lun Huang,Ercan Engin Kuruoglu,Jimeng Sun,Xi Chen,Yefeng Zheng | ICLR 2022,Spotlight | Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works trying to derive non-vacuous bounds for NNs. Recently, the compression of information stored in weights (IIW) is proved to play a key role in NNs generalization based on the PAC-Bayes theorem. However, no solution of IIW has ever been provided, which builds a barrier for further investigation of the IIW's property and its potential in practical deep learning. In this paper, we propose an algorithm for the efficient approximation of IIW. Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB. From PIB, we can empirically identify the fitting to compressing phase transition during NNs' training and the concrete connection between the IIW compression and the generalization. Besides, we verify that IIW is able to explain NNs in broad cases, e.g., varying batch sizes, over-parameterization, and noisy labels. Moreover, we propose an MCMC-based algorithm to sample from the optimal weight posterior characterized by PIB, which fulfills the potential of IIW in enhancing NNs in practice. | https://openreview.net/pdf/3c2adeb5d32bd783ea4cbafee0397d6e76f81ce7.pdf |
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing | https://openreview.net/forum?id=jXKKDEi5vJt | https://openreview.net/forum?id=jXKKDEi5vJt | Sai Praneeth Karimireddy,Lie He,Martin Jaggi | ICLR 2022,Spotlight | In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers. However, a fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. While this problem has received significant attention recently, most current defenses assume that the workers have identical data. For realistic cases when the data across workers are heterogeneous (non-iid), we design new attacks which circumvent current defenses, leading to significant loss of performance. We then propose a simple bucketing scheme that adapts existing robust algorithms to heterogeneous datasets at a negligible computational cost. We also theoretically and experimentally validate our approach, showing that combining bucketing with existing robust algorithms is effective against challenging attacks. Our work is the first to establish guaranteed convergence for the non-iid Byzantine robust problem under realistic assumptions.
| https://openreview.net/pdf/def52b4b7685b406e3aa64994f6e04b41df63bdb.pdf |
Understanding the Role of Self Attention for Efficient Speech Recognition | https://openreview.net/forum?id=AvcfxqRy4Y | https://openreview.net/forum?id=AvcfxqRy4Y | Kyuhong Shim,Jungwook Choi,Wonyong Sung | ICLR 2022,Spotlight | Self-attention (SA) is a critical component of Transformer neural networks that have succeeded in automatic speech recognition (ASR). In this paper, we analyze the role of SA in Transformer-based ASR models for not only understanding the mechanism of improved recognition accuracy but also lowering the computational complexity. We reveal that SA performs two distinct roles: phonetic and linguistic localization. Especially, we show by experiments that phonetic localization in the lower layers extracts phonologically meaningful features from speech and reduces the phonetic variance in the utterance for proper linguistic localization in the upper layers. From this understanding, we discover that attention maps can be reused as long as their localization capability is preserved. To evaluate this idea, we implement the layer-wise attention map reuse on real GPU platforms and achieve up to 1.96 times speedup in inference and 33% savings in training time with noticeably improved ASR performance for the challenging benchmark on LibriSpeech dev/test-other dataset.
| https://openreview.net/pdf/d785690241796686225be6fa4f299ba32712c574.pdf |
Label Encoding for Regression Networks | https://openreview.net/forum?id=8WawVDdKqlL | https://openreview.net/forum?id=8WawVDdKqlL | Deval Shah,Zi Yu Xue,Tor Aamodt | ICLR 2022,Spotlight | Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolute error of output labels. Prior work has shown that solving a regression problem with a set of binary classifiers can improve accuracy by utilizing well-studied binary classification algorithms. We introduce binary-encoded labels (BEL), which generalizes the application of binary classification to regression by providing a framework for considering arbitrary multi-bit values when encoding target values. We identify desirable properties of suitable encoding and decoding functions used for the conversion between real-valued and binary-encoded labels based on theoretical and empirical study. These properties highlight a tradeoff between classification error probability and error-correction capabilities of label encodings. BEL can be combined with off-the-shelf task-specific feature extractors and trained end-to-end. We propose a series of sample encoding, decoding, and training loss functions for BEL and demonstrate they result in lower error than direct regression and specialized approaches while being suitable for a diverse set of regression problems, network architectures, and evaluation metrics. BEL achieves state-of-the-art accuracies for several regression benchmarks. Code is available at https://github.com/ubc-aamodt-group/BEL_regression.
| https://openreview.net/pdf/f9f19a0a8f967030ba6a2061f7e7c78524762ef7.pdf |
Equivariant Transformers for Neural Network based Molecular Potentials | https://openreview.net/forum?id=zNHzqZ9wrRB | https://openreview.net/forum?id=zNHzqZ9wrRB | Philipp Thölke,Gianni De Fabritiis | ICLR 2022,Spotlight | The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed. Machine learning potentials have previously shown great success in this domain, reaching increasingly better accuracy while maintaining computational efficiency comparable with classical force fields. In this work we propose TorchMD-NET, a novel equivariant Transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1, and many QM9 targets in both accuracy and computational efficiency. Through an extensive attention weight analysis, we gain valuable insights into the black box predictor and show differences in the learned representation of conformers versus conformations sampled from molecular dynamics or normal modes. Furthermore, we highlight the importance of datasets including off-equilibrium conformations for the evaluation of molecular potentials. | https://openreview.net/pdf/3567cf90fbb34ac3013bdb0c392a4d115421dec8.pdf |
SGD Can Converge to Local Maxima | https://openreview.net/forum?id=9XhPLAjjRB | https://openreview.net/forum?id=9XhPLAjjRB | Liu Ziyin,Botao Li,James B Simon,Masahito Ueda | ICLR 2022,Spotlight | Previous works on stochastic gradient descent (SGD) often focus on its success. In this work, we construct worst-case optimization problems illustrating that, when not in the regimes that the previous works often assume, SGD can exhibit many strange and potentially undesirable behaviors. Specifically, we construct landscapes and data distributions such that (1) SGD converges to local maxima, (2) SGD escapes saddle points arbitrarily slowly, (3) SGD prefers sharp minima over flat ones, and (4) AMSGrad converges to local maxima. We also realize results in a minimal neural network-like example. Our results highlight the importance of simultaneously analyzing the minibatch sampling, discrete-time updates rules, and realistic landscapes to understand the role of SGD in deep learning. | https://openreview.net/pdf/c054a998fd46a7c8498a497ba6b856c2e0532b6b.pdf |
Hybrid Local SGD for Federated Learning with Heterogeneous Communications | https://openreview.net/forum?id=H0oaWl6THa | https://openreview.net/forum?id=H0oaWl6THa | Yuanxiong Guo,Ying Sun,Rui Hu,Yanmin Gong | ICLR 2022,Spotlight | Communication is a key bottleneck in federated learning where a large number of edge devices collaboratively learn a model under the orchestration of a central server without sharing their own training data. While local SGD has been proposed to reduce the number of FL rounds and become the algorithm of choice for FL, its total communication cost is still prohibitive when each device needs to communicate with the remote server repeatedly for many times over bandwidth-limited networks. In light of both device-to-device (D2D) and device-to-server (D2S) cooperation opportunities in modern communication networks, this paper proposes a new federated optimization algorithm dubbed hybrid local SGD (HL-SGD) in FL settings where devices are grouped into a set of disjoint clusters with high D2D communication bandwidth. HL-SGD subsumes previous proposed algorithms such as local SGD and gossip SGD and enables us to strike the best balance between model accuracy and runtime. We analyze the convergence of HL-SGD in the presence of heterogeneous data for general nonconvex settings. We also perform extensive experiments and show that the use of hybrid model aggregation via D2D and D2S communications in HL-SGD can largely speed up the training time of federated learning. | https://openreview.net/pdf/e765632eb816448e521a3fb0562e39c39651de07.pdf |
Increasing the Cost of Model Extraction with Calibrated Proof of Work | https://openreview.net/forum?id=EAy7C1cgE1L | https://openreview.net/forum?id=EAy7C1cgE1L | Adam Dziedzic,Muhammad Ahmad Kaleem,Yu Shen Lu,Nicolas Papernot | ICLR 2022,Spotlight | In model extraction attacks, adversaries can steal a machine learning model exposed via a public API by repeatedly querying it and adjusting their own model based on obtained predictions. To prevent model stealing, existing defenses focus on detecting malicious queries, truncating, or distorting outputs, thus necessarily introducing a tradeoff between robustness and model utility for legitimate users. Instead, we propose to impede model extraction by requiring users to complete a proof-of-work before they can read the model's predictions. This deters attackers by greatly increasing (even up to 100x) the computational effort needed to leverage query access for model extraction. Since we calibrate the effort required to complete the proof-of-work to each query, this only introduces a slight overhead for regular users (up to 2x). To achieve this, our calibration applies tools from differential privacy to measure the information revealed by a query. Our method requires no modification of the victim model and can be applied by machine learning practitioners to guard their publicly exposed models against being easily stolen. | https://openreview.net/pdf/c75fb6727a40e11e23ec143778cd6e178a0681f3.pdf |
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks | https://openreview.net/forum?id=HFmAukZ-k-2 | https://openreview.net/forum?id=HFmAukZ-k-2 | Marten Lienen,Stephan Günnemann | ICLR 2022,Spotlight | We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we derive a continuous-time model for the dynamics of the data via the finite element method. The resulting graph neural network estimates the instantaneous effects of the unknown dynamics on each cell in a meshing of the spatial domain. Our model can incorporate prior knowledge via assumptions on the form of the unknown PDE, which induce a structural bias towards learning specific processes. Through this mechanism, we derive a transport variant of our model from the convection equation and show that it improves the transfer performance to higher-resolution meshes on sea surface temperature and gas flow forecasting against baseline models representing a selection of spatio-temporal forecasting methods. A qualitative analysis shows that our model disentangles the data dynamics into their constituent parts, which makes it uniquely interpretable. | https://openreview.net/pdf/ded4dea1523b16ecaee30b3bb0676b5556db6544.pdf |
Probabilistic Implicit Scene Completion | https://openreview.net/forum?id=BnQhMqDfcKG | https://openreview.net/forum?id=BnQhMqDfcKG | Dongsu Zhang,Changwoon Choi,Inbum Park,Young Min Kim | ICLR 2022,Spotlight | We propose a probabilistic shape completion method extended to the continuous geometry of large-scale 3D scenes. Real-world scans of 3D scenes suffer from a considerable amount of missing data cluttered with unsegmented objects. The problem of shape completion is inherently ill-posed, and high-quality result requires scalable solutions that consider multiple possible outcomes. We employ the Generative Cellular Automata that learns the multi-modal distribution and transform the formulation to process large-scale continuous geometry. The local continuous shape is incrementally generated as a sparse voxel embedding, which contains the latent code for each occupied cell. We formally derive that our training objective for the sparse voxel embedding maximizes the variational lower bound of the complete shape distribution and therefore our progressive generation constitutes a valid generative model. Experiments show that our model successfully generates diverse plausible scenes faithful to the input, especially when the input suffers from a significant amount of missing data. We also demonstrate that our approach outperforms deterministic models even in less ambiguous cases with a small amount of missing data, which infers that probabilistic formulation is crucial for high-quality geometry completion on input scans exhibiting any levels of completeness. | https://openreview.net/pdf/d9aba3c3efb0ad334f7b6a755f29a0b39bc05867.pdf |
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters | https://openreview.net/forum?id=7l1IjZVddDW | https://openreview.net/forum?id=7l1IjZVddDW | Qiang Meng,Feng Zhou,Hainan Ren,Tianshu Feng,Guochao Liu,Yuanqing Lin | ICLR 2022,Spotlight | The growing public concerns on data privacy in face recognition can be partly relieved by the federated learning (FL) paradigm. However, conventional FL methods usually perform poorly due to the particularity of the task, \textit{i.e.}, broadcasting class centers among clients is essential for recognition performances but leads to privacy leakage. To resolve the privacy-utility paradox, this work proposes PrivacyFace, a framework largely improves the federated learning face recognition via communicating auxiliary and privacy-agnostic information among clients. PrivacyFace mainly consists of two components: First, a practical Differentially Private Local Clustering (DPLC) mechanism is proposed to distill sanitized clusters from local class centers. Second, a consensus-aware recognition loss subsequently encourages global consensuses among clients, which ergo leads to more discriminative features. The proposed schemes are mathematically proved to be differential private, introduce a lightweight overhead as well as yield prominent performance boosts (\textit{e.g.}, +9.63\% and +10.26\% for TAR@FAR=1e-4 on IJB-B and IJB-C respectively). Extensive experiments and ablation studies on a large-scale dataset have demonstrated the efficacy and practicability of our method. | https://openreview.net/pdf/6e32e9b384f0d8b5260a2a95ad7645cebf50046f.pdf |
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