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d251279905 | Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, in light of computational intractability barriers in general-sum games, provable guarantees have been thus far either limited to fully competitive or cooperative scenarios or impose strong assumptions that are difficult to meet in most practical applications.In this work, we depart from those prior results by investigating infinite-horizon adversarial team Markov games, a natural and well-motivated class of games in which a team of identically-interested players-in the absence of any explicit coordination or communication-is competing against an adversarial player. This setting allows for a unifying treatment of zero-sum Markov games and Markov potential games, and serves as a step to model more realistic strategic interactions that feature both competing and cooperative interests. Our main contribution is the first algorithm for computing stationary ǫ-approximate Nash equilibria in adversarial team Markov games with computational complexity that is polynomial in all the natural parameters of the game, as well as 1/ǫ.The proposed algorithm is particularly natural and practical, and it is based on performing independent policy gradient steps for each player in the team, in tandem with best responses from the side of the adversary; in turn, the policy for the adversary is then obtained by solving a carefully constructed linear program. Our analysis leverages non-standard techniques to establish the KKT optimality conditions for a nonlinear program with nonconvex constraints, thereby leading to a natural interpretation of the induced Lagrange multipliers. Along the way, we significantly extend an important characterization of optimal policies in adversarial (normal-form) team games due to Von Stengel and Koller (GEB '97). | Efficiently Computing Nash Equilibria in Adversarial Team Markov Games |
d229677982 | We study adversary-resilient stochastic distributed optimization, in which m machines can independently compute stochastic gradients, and cooperate to jointly optimize over their local objective functions. However, an α-fraction of the machines are Byzantine, in that they may behave in arbitrary, adversarial ways. We consider a variant of this procedure in the challenging non-convex case. Our main result is a new algorithm SafeguardSGD which can provably escape saddle points and find approximate local minima of the non-convex objective. The algorithm is based on a new concentration filtering technique, and its sample and time complexity bounds match the best known theoretical bounds in the stochastic, distributed setting when no Byzantine machines are present. Our algorithm is practical: it improves upon the performance of prior methods when training deep neural networks, it is relatively lightweight, and is the first method to withstand two recently-proposed Byzantine attacks. * V1 appears on this date on openreview and V1.5 polishes writing. We would like to thank Chi Jin and Dong Yin for very insightful discussions on this subject, and an anonymous reviewer who suggested a simpler proof. | Byzantine-Resilient Non-Convex Stochastic Gradient Descent |
d53316620 | This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent evaluation in reinforcement learning, Vanilla Monte Carlo, can miss failures entirely, leading to the deployment of unsafe agents. We demonstrate this is an issue for current agents, where even matching the compute used for training is sometimes insufficient for evaluation. To address this shortcoming, we draw upon the rare event probability estimation literature and propose an adversarial evaluation approach. Our approach focuses evaluation on adversarially chosen situations, while still providing unbiased estimates of failure probabilities. The key difficulty is in identifying these adversarial situations -since failures are rare there is little signal to drive optimization. To solve this we propose a continuation approach that learns failure modes in related but less robust agents. Our approach also allows reuse of data already collected for training the agent. We demonstrate the efficacy of adversarial evaluation on two standard domains: humanoid control and simulated driving. Experimental results show that our methods can find catastrophic failures and estimate failures rates of agents multiple orders of magnitude faster than standard evaluation schemes, in minutes to hours rather than days. | RIGOROUS AGENT EVALUATION: AN ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES |
d256194369 | Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous works formalize such problems as Model Editing (ME) and mostly focus on fixing one mistake. However, the one-mistake-fixing scenario is not an accurate abstraction of the real-world challenge. In the deployment of AI services, there are ever-emerging mistakes, and the same mistake may recur if not corrected in time. Thus a preferable solution is to rectify the mistakes as soon as they appear nonstop. Therefore, we extend the existing ME into Sequential Model Editing (SME) to help develop more practical editing methods. Our study shows that most current ME methods could yield unsatisfying results in this scenario. We then introduce Transformer-Patcher, a novel model editor that can shift the behavior of transformer-based models by simply adding and training a few neurons in the last Feed-Forward Network layer. Experimental results on both classification and generation tasks show that Transformer-Patcher can successively correct up to thousands of errors (Reliability) and generalize to their equivalent inputs (Generality) while retaining the model's accuracy on irrelevant inputs (Locality). Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME). The code is available at https://github.com/ZeroYuHuang/Transform er-Patcher.Published as a conference paper at ICLR 2023 the model through a HyperNetwork, which regards the model and the false predicted example as inputs and produced a weight update for the model's parameters(Cao et al., 2021;Sinitsin et al., 2020; Mitchell et al., 2022a). Despite their impressive progress, they mostly focus on one-step editing (fixing one mistake), which is not applicable to practical situations. Because models deployed for real-world applications are expected to face different errors ceaselessly. And the same error may pop up repeatedly and bother different users. In addition, as illustrated inFigure 1, once a wrong answer appears in an online question-answering (QA) model, leaving it unfixed and waiting for future corrections could mislead more people. Therefore, an ideal model editor should provide continuous and promptly fixing of newly emerged mistakes in an effective and efficient manner. | TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON |
d238634325 | Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off is critical, because most current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy, and therefore need to either constrain these actions to be in-distribution, or else regularize their values. We propose a new offline RL method that never needs to evaluate actions outside of the dataset, but still enables the learned policy to improve substantially over the best behavior in the data through generalization. The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state. This leverages the generalization capacity of the function approximator to estimate the value of the best available action at a given state without ever directly querying a Q-function with this unseen action. Our algorithm alternates between fitting this upper expectile value function and backing it up into a Q-function, without any explicit policy. Then, we extract the policy via advantage-weighted behavioral cloning, which also avoids querying out-of-sample actions. We dub our method implicit Q-learning (IQL). IQL is easy to implement, computationally efficient, and only requires fitting an additional critic with an asymmetric L2 loss. 1 IQL demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline reinforcement learning. We also demonstrate that IQL achieves strong performance fine-tuning using online interaction after offline initialization. | OFFLINE REINFORCEMENT LEARNING WITH IMPLICIT Q-LEARNING |
d13253311 | In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs. In the natural sciences, however, finding an interpretable function for a phenomenon is the prime goal as it allows to understand and generalize results. This paper proposes a novel type of function learning network, called equation learner (EQL), that can learn analytical expressions and is able to extrapolate to unseen domains. It is implemented as an end-to-end differentiable feed-forward network and allows for efficient gradient based training. Due to sparsity regularization concise interpretable expressions can be obtained. Often the true underlying source expression is identified. | Extrapolation and learning equations |
d209960830 | We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size -up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics. | LEARNING HEURISTICS FOR QUANTIFIED BOOLEAN FORMULAS THROUGH REINFORCEMENT LEARNING |
d170079235 | Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to a third dimension (using a limited number of space-time modules such as 3D convolutions) or by introducing a handcrafted two-stream design to capture both appearance and motion in videos. We interpret a video CNN as a collection of multi-stream space-time convolutional blocks connected to each other, and propose the approach of automatically finding neural architectures with better connectivity for video understanding. This is done by evolving a population of overly-connected architectures guided by connection weight learning. Architectures combining representations that abstract different input types (i.e., RGB and optical flow) at multiple temporal resolutions are searched for, allowing different types or sources of information to interact with each other. Our method, referred to as AssembleNet, outperforms prior approaches on public video datasets, in some cases by a great margin.Preprint. Under review. | AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures |
d48365044 | Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence. Nonetheless, popular tasks such as speech or images recognition, involve multi-dimensional input features that are characterized by strong internal dependencies between the dimensions of the input vector. We propose a novel quaternion recurrent neural network (QRNN), alongside with a quaternion long-short term memory neural network (QLSTM), that take into account both the external relations and these internal structural dependencies with the quaternion algebra. Similarly to capsules, quaternions allow the QRNN to code internal dependencies by composing and processing multidimensional features as single entities, while the recurrent operation reveals correlations between the elements composing the sequence. We show that both QRNN and QLSTM achieve better performances than RNN and LSTM in a realistic application of automatic speech recognition. Finally, we show that QRNN and QLSTM reduce by a maximum factor of 3.3x the number of free parameters needed, compared to real-valued RNNs and LSTMs to reach better results, leading to a more compact representation of the relevant information. * CIFAR Senior Fellow | QUATERNION RECURRENT NEURAL NETWORKS |
d235377428 | Generative models are now capable of producing highly realistic images that look nearly indistinguishable from the data on which they are trained.This raises the question: if we have good enough generative models, do we still need datasets?We investigate this question in the setting of learning general-purpose visual representations from a black-box generative model rather than directly from data.Given an off-the-shelf image generator without any access to its training data, we train representations from the samples output by this generator.We compare several representation learning methods that can be applied to this setting, using the latent space of the generator to generate multiple "views" of the same semantic content.We show that for contrastive methods, this multiview data can naturally be used to identify positive pairs (nearby in latent space) and negative pairs (far apart in latent space).We find that the resulting representations rival or even outperform those learned directly from real data, but that good performance requires care in the sampling strategy applied and the training method.Generative models can be viewed as a compressed and organized copy of a dataset, and we envision a future where more and more "model zoos" proliferate while datasets become increasingly unwieldy, missing, or private.This paper suggests several techniques for dealing with visual representation learning in such a future.Code is available on our project page https://ali-design.github.io/GenRep/. | GENERATIVE MODELS AS A DATA SOURCE FOR MULTIVIEW REPRESENTATION LEARNING |
d244129839 | Recent work has shown that models trained to the same objective, and which achieve similar measures of accuracy on consistent test data, may nonetheless behave very differently on individual predictions. This inconsistency is undesirable in high-stakes contexts, such as medical diagnosis and finance. We show that this inconsistentbehavior extends beyond predictions to feature attributions, which may likewise have negative implications for the intelligibility of a model, and one's ability to find recourse for subjects. We then introduce selective ensembles to mitigate such inconsistencies by applying hypothesis testing to the predictions of a set of models trained using randomly-selected starting conditions; importantly, selective ensembles can abstain in cases where a consistent outcome cannot be achieved up to a specified confidence level. We prove that that prediction disagreement between selective ensembles is bounded, and empirically demonstrate that selective ensembles achieve consistent predictions and feature attributions while maintaining low abstention rates. On several benchmark datasets, selective ensembles reach zero inconsistently predicted points, with abstention rates as low 1.5%.PreprintDheeru Dua and Efi Karra Taniskidou. UCI machine learning repository. https:/ive.ics.uci.edu/ml, 2017. | SELECTIVE ENSEMBLES FOR CONSISTENT PREDICTIONS |
d253581623 | Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture. These applications typically assume that the domain symmetry is fully described by explicit transformations of the model inputs and outputs. However, many real-life applications contain only latent or partial symmetries which cannot be easily described by simple transformations of the input. In these cases, it is necessary to learn symmetry in the environment instead of imposing it mathematically on the network architecture. We discover, surprisingly, that imposing equivariance constraints that do not exactly match the domain symmetry is very helpful in learning the true symmetry in the environment. We differentiate between extrinsic and incorrect symmetry constraints and show that while imposing incorrect symmetry can impede the model's performance, imposing extrinsic symmetry can actually improve performance. We demonstrate that an equivariant model can significantly outperform non-equivariant methods on domains with latent symmetries both in supervised learning and in reinforcement learning for robotic manipulation and control problems. * Equal Advising arXiv:2211.09231v2 [cs.LG] 10 Feb 2023Published as a conference paper at ICLR 2023 example, suppose we want to model a function with the object-wise rotation symmetry expressed inFigure 1aand b. Notice that whereas it is difficult to encode the object-wise symmetry, it is easy to encode an image-wise symmetry because it involves simple image rotations. Although the imagewise symmetry model is imprecise in this situation, our experiments indicate that this imprecise model is still a much better choice than a completely unstructured model. This paper makes three contributions. First, we define three different relationships between problem symmetry and model symmetry: correct equivariance, incorrect equivariance, and extrinsic equivariance. Correct equivariance means the model correctly models the problem symmetry; incorrect equivariance is when the model symmetry interferes with the problem symmetry; and extrinsic equivariance is when the model symmetry transforms the input data to outof-distribution data. We theoretically demonstrate the upper bound performance for an incorrectly constrained equivariant model. Second, we empirically compare extrinsic and incorrect equivariance in a supervised learning task and show that a model with extrinsic equivariance can improve performance compared with an unconstrained model. Finally, we explore this idea in a reinforcement learning context and show that an extrinsically constrained model can outperform state-of-the-art conventional CNN baselines. Supplementary video and code are available at https://pointw.github.io/extrinsic_page/. | THE SURPRISING EFFECTIVENESS OF EQUIVARIANT MODELS IN DOMAINS WITH LATENT SYMMETRY |
d211126562 | Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. In particular, Dyna is an elegant model-based architecture integrating learning and planning that provides huge flexibility of using a model. One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences. Searchcontrol is critical in improving learning efficiency. In this work, we propose a simple and novel search-control strategy by searching high frequency regions of the value function. Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct. We empirically show that a high frequency function is more difficult to approximate. This suggests a search-control strategy: we should use states from high frequency regions of the value function to query the model to acquire more samples. We develop a simple strategy to locally measure the frequency of a function by gradient and hessian norms, and provide theoretical justification for this approach. We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains. * Equal contribution.Published as a conference paper at ICLR 2020 model errors too, which causes some performance deterioration(Talvitie, 2014;2017). Without an elegant search-control mechanism, we are not likely to benefit from the flexibility given by a model. | FREQUENCY-BASED SEARCH-CONTROL IN DYNA |
d235358966 | Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge. However, the bottleneck of current meta-learning algorithms is the requirement of a large number of meta-training tasks, which may not be accessible in real-world scenarios. To address the challenge that available tasks may not densely sample the space of tasks, we propose to augment the task set through interpolation. By meta-learning with task interpolation (MLTI), our approach effectively generates additional tasks by randomly sampling a pair of tasks and interpolating the corresponding features and labels. Under both gradient-based and metric-based meta-learning settings, our theoretical analysis shows MLTI corresponds to a data-adaptive meta-regularization and further improves the generalization. Empirically, in our experiments on eight datasets from diverse domains including image recognition, pose prediction, molecule property prediction, and medical image classification, we find that the proposed general MLTI framework is compatible with representative meta-learning algorithms and consistently outperforms other state-of-the-art strategies.Recently, a variety of regularization methods for meta-learning have been proposed, including techniques that impose explicit regularization to the meta-learning model (Jamal and Qi, 2019;Yin et al., 2020)and methods that augment tasks by making modifications to individual training tasks through noise (Lee et al., 2020) or mixup (Ni et al., 2021; Yao et al., 2021). However, these methods are largely designed to either tackle only the memorization problem(Yin et al., 2020)or to improve performance of meta-learning (Yao et al., 2021) when plenty of meta-training tasks are provided. Instead, we aim to target the task distribution directly, leading to an approach that is particularly well-suited to settings with limited meta-training tasks.Concretely, as illustrated inFigure 1, we aim to densify the task distribution by providing interpolated tasks across meta-training tasks, resulting in a new task interpolation algorithm named MLTI (Meta-Learning with Task Interpolation). The key idea behind MLTI is to generate new tasks by interpolating between pairs of randomly sampled meta-training tasks. This interpolation can be instantiated in a variety of ways, and we present two variants that we find to be particularly effective.We use CutMix (Yun et al., 2019) to interpolate samples in the above three image classification datasets. Besides, the interpolation strategy is applied on the query set when i = j, which empirically achieves better performance. Follow (Cao et al., 2021), the Tabular Murris dataset is collected from 23 organs, which contains 105,960 cells of 124 cell types. We aim to classify the cell type of each cell, which is represented by 2,866 genes (i.e, the dimension of features is 2,866). We use the code ofCao et al. (2021)to construct tasks, where 15/4/4 organs are selected for meta-training/validation/testing. The selected organs are detailed as follows:Tabular Murris.Meta-training organs:In Tabular Murris, the base model contains two fully connected blocks and a linear regressor, where each fully connected block contains a linear layer, a batch normalization layer, a ReLU activation layer, and a dropout layer. FollowCao et al. (2021), the default dropout ratio and the output channels | META-LEARNING WITH FEWER TASKS THROUGH TASK INTERPOLATION |
d238743895 | We study non-convex subgradient flows for training two-layer ReLU neural networks from a convex geometry and duality perspective. We characterize the implicit bias of unregularized non-convex gradient flow as convex regularization of an equivalent convex model. We then show that the limit points of non-convex subgradient flows can be identified via primal-dual correspondence in this convex optimization problem. Moreover, we derive a sufficient condition on the dual variables which ensures that the stationary points of the non-convex objective are the KKT points of the convex objective, thus proving convergence of non-convex gradient flows to the global optimum. For a class of regular training data distributions such as orthogonal separable data, we show that this sufficient condition holds. Therefore, non-convex gradient flows in fact converge to optimal solutions of a convex optimization problem. We present numerical results verifying the predictions of our theory for non-convex subgradient descent. | The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program |
d259841016 | Semi-implicit variational inference (SIVI) greatly enriches the expressiveness of variational families by considering implicit variational distributions defined in a hierarchical manner. However, due to the intractable densities of variational distributions, current SIVI approaches often use surrogate evidence lower bounds (EL-BOs) or employ expensive inner-loop MCMC runs for direct ELBO maximization for training. In this paper, we propose SIVI-SM, a new method for SIVI based on an alternative training objective via score matching. Leveraging the hierarchical structure of semi-implicit variational families, the score matching objective allows a minimax formulation where the intractable variational densities can be naturally handled with denoising score matching. We show that SIVI-SM closely matches the accuracy of MCMC and outperforms ELBO-based SIVI methods in a variety of Bayesian inference tasks. | SEMI-IMPLICIT VARIATIONAL INFERENCE VIA SCORE MATCHING |
d261031179 | Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes, etc. We show that, for a subset of relations, this computation is well-approximated by a single linear transformation on the subject representation. Linear relation representations may be obtained by constructing a first-order approximation to the LM from a single prompt, and they exist for a variety of factual, commonsense, and linguistic relations. However, we also identify many cases in which LM predictions capture relational knowledge accurately, but this knowledge is not linearly encoded in their representations. Our results thus reveal a simple, interpretable, but heterogeneously deployed knowledge representation strategy in transformer LMs. | Linearity of Relation Decoding in Transformer Language Models |
d247778706 | Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search. One-Shot methods tackle this challenge by training one supernet to approximate the performance of every architecture in the search space via weight-sharing, thereby drastically reducing the search cost. However, due to coupled optimization between child architectures caused by weight-sharing, One-Shot supernet's performance estimation could be inaccurate, leading to degraded search outcomes. To address this issue, Few-Shot NAS reduces the level of weight-sharing by splitting the One-Shot supernet into multiple separated sub-supernets via edge-wise (layer-wise) exhaustive partitioning. Since each partition of the supernet is not equally important, it necessitates the design of a more effective splitting criterion. In this work, we propose a gradient matching score (GM) that leverages gradient information at the shared weight for making informed splitting decisions. Intuitively, gradients from different child models can be used to identify whether they agree on how to update the shared modules, and subsequently to decide if they should share the same weight. Compared with exhaustive partitioning, the proposed criterion significantly reduces the branching factor per edge. This allows us to split more edges (layers) for a given budget, resulting in substantially improved performance as NAS search spaces usually include dozens of edges (layers). Extensive empirical evaluations of the proposed method on a wide range of search spaces (NASBench-201, DARTS, MobileNet Space), datasets (cifar10, cifar100, ImageNet) and search algorithms (DARTS, SNAS, RSPS, ProxylessNAS, OFA) demonstrate that it significantly outperforms its Few-Shot counterparts while surpassing previous comparable methods in terms of the accuracy of derived architectures. Our code is available at https://github.com/skhu101/GM-NAS. | GENERALIZING FEW-SHOT NAS WITH GRADIENT MATCHING |
d225075792 | We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradient updates decrease the expressivity of the current value network. We characterize this loss of expressivity via a drop in the rank of the learned value network features, and show that this typically corresponds to a performance drop. We demonstrate this phenomenon on Atari and Gym benchmarks, in both offline and online RL settings. We formally analyze this phenomenon and show that it results from a pathological interaction between bootstrapping and gradient-based optimization. We further show that mitigating implicit under-parameterization by controlling rank collapse can improve performance. | IMPLICIT UNDER-PARAMETERIZATION INHIBITS DATA-EFFICIENT DEEP REINFORCEMENT LEARNING |
d259088961 | Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into a model's predictions.The problem is challenging, however, as it requires both predicting with arbitrary feature sets and learning a policy to identify valuable selections.Here, we take an informationtheoretic perspective and prioritize features based on their mutual information with the response variable.The main challenge is implementing this policy, and we design a new approach that estimates the mutual information in a discriminative rather than generative fashion.Building on our approach, we then introduce several further improvements: allowing variable feature budgets across samples, enabling non-uniform feature costs, incorporating prior information, and exploring modern architectures to handle partial inputs.Our experiments show that our method provides consistent gains over recent methods across a variety of datasets.* Equal contribution. 1 Prior works have also referred to the problem as as sequential information maximization ( | ESTIMATING CONDITIONAL MUTUAL INFORMATION FOR DYNAMIC FEATURE SELECTION |
d220404588 | Differentially private SGD (DP-SGD) is one of the most popular methods for solving differentially private empirical risk minimization (ERM). Due to its noisy perturbation on each gradient update, the error rate of DP-SGD scales with the ambient dimension p, the number of parameters in the model. Such dependence can be problematic for over-parameterized models where p n, the number of training samples. Existing lower bounds on private ERM show that such dependence on p is inevitable in the worst case. In this paper, we circumvent the dependence on the ambient dimension by leveraging a low-dimensional structure of gradient space in deep networks-that is, the stochastic gradients for deep nets usually stay in a low dimensional subspace in the training process. We propose Projected DP-SGD that performs noise reduction by projecting the noisy gradients to a low-dimensional subspace, which is given by the top gradient eigenspace on a small public dataset. We provide a general sample complexity analysis on the public dataset for the gradient subspace identification problem and demonstrate that under certain low-dimensional assumptions the public sample complexity only grows logarithmically in p. Finally, we provide a theoretical analysis and empirical evaluations to show that our method can substantially improve the accuracy of DP-SGD. | Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification |
d229363613 | Existing literature in Continual Learning (CL) has focused on overcoming catastrophic forgetting, the inability of the learner to recall how to perform tasks observed in the past. There are however other desirable properties of a CL system, such as the ability to transfer knowledge from previous tasks and to scale memory and compute sub-linearly with the number of tasks. Since most current benchmarks focus only on forgetting using short streams of tasks, we first propose a new suite of benchmarks to probe CL algorithms across these new axes. Finally, we introduce a new modular architecture, whose modules represent atomic skills that can be composed to perform a certain task. Learning a task reduces to figuring out which past modules to re-use, and which new modules to instantiate to solve the current task. Our learning algorithm leverages a task-driven prior over the exponential search space of all possible ways to combine modules, enabling efficient learning on long streams of tasks. Our experiments show that this modular architecture and learning algorithm perform competitively on widely used CL benchmarks while yielding superior performance on the more challenging benchmarks we introduce in this work. | EFFICIENT CONTINUAL LEARNING WITH MODULAR NETWORKS AND TASK-DRIVEN PRIORS |
d257353362 | Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. Probabilistic rotation regression has raised more and more attention with the benefit of expressing uncertainty information along with the prediction. Though modeling noise using Gaussian-resembling Bingham distribution and matrix Fisher distribution is natural, they are shown to be sensitive to outliers for the nature of quadratic punishment to deviations. In this paper, we draw inspiration from multivariate Laplace distribution and propose a novel Rotation Laplace distribution on SO(3). Rotation Laplace distribution is robust to the disturbance of outliers and enforces much gradient to the low-error region, resulting in a better convergence. Our extensive experiments show that our proposed distribution achieves state-of-the-art performance for rotation regression tasks over both probabilistic and non-probabilistic baselines. Our project page is at pkuepic.github.io/RotationLaplace. † He Wang and Baoquan Chen are the corresponding authors ({hewang, baoquan}@pku.edu.cn).Suppose the singular value decomposition of matrix A is given byBingham. An antipodally symmetric distribution on the sphere.Gregory S Chirikjian. Engineering applications of noncommutative harmonic analysis: with emphasis on rotation and motion groups. CRC press, 2000.John L Crassidis and F Landis Markley. Unscented filtering for spacecraft attitude estimation. Journal of guidance, control, and dynamics, 26(4): [536][537][538][539][540][541][542] 2003. | A LAPLACE-INSPIRED DISTRIBUTION ON SO(3) FOR PROBABILISTIC ROTATION ESTIMATION |
d264490556 | Positioned between pre-training and user deployment, aligning large language models (LLMs) through reinforcement learning (RL) has emerged as a prevailing strategy for training instruction following-models such as ChatGPT.In this work, we initiate the study of privacy-preserving alignment of LLMs through Differential Privacy (DP) in conjunction with RL.Following the influential work of Ziegler et al. (2020), we study two dominant paradigms: (i) alignment via RL without human in the loop (e.g., positive review generation) and (ii) alignment via RL from human feedback (RLHF) (e.g., summarization in a human-preferred way).We give a new DP framework to achieve alignment via RL, and prove its correctness.Our experimental results validate the effectiveness of our approach, offering competitive utility while ensuring strong privacy protections. | Privately Aligning Language Models with Reinforcement Learning |
d257687205 | Machine Learning (ML) models have been utilized for malware detection for over two decades.Consequently, this ignited an ongoing arms race between malware authors and antivirus systems, compelling researchers to propose defenses for malware-detection models against evasion attacks.However, most if not all existing defenses against evasion attacks suffer from sizable performance degradation and/or can defend against only specific attacks, which makes them less practical in real-world settings.In this work, we develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection.Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables.After showing how DRSM is theoretically robust against attacks with contiguous adversarial bytes, we verify its performance and certified robustness experimentally, where we observe only marginal accuracy drops as the cost of robustness.To our knowledge, we are the first to offer certified robustness in the realm of static detection of malware executables.More surprisingly, through evaluating DRSM against 9 empirical attacks of different types, we observe that the proposed defense is empirically robust to some extent against a diverse set of attacks, some of which even fall out of the scope of its original threat model.In addition, we collected 15.5K recent benign raw executables from diverse sources, which will be made public as a dataset called PACE (Publicly Accessible Collection(s) of Executables) to alleviate the scarcity of publicly available benign datasets for studying malware detection and provide future research with more representative data of the time. | DRSM: DE-RANDOMIZED SMOOTHING ON MALWARE CLASSIFIER PROVIDING CERTIFIED ROBUSTNESS |
d220363897 | Many successful deep learning architectures are equivariant to certain transformations in order to conserve parameters and improve generalization: most famously, convolution layers are equivariant to shifts of the input. This approach only works when practitioners know a priori symmetries of the task and can manually construct an architecture with the corresponding equivariances. Our goal is a general approach for learning equivariances from data, without needing prior knowledge of a task's symmetries or custom task-specific architectures. We present a method for learning and encoding equivariances into networks by learning corresponding parameter sharing patterns from data. Our method can provably encode equivarianceinducing parameter sharing for any finite group of symmetry transformations, and we find experimentally that it can automatically learn a variety of equivariances from symmetries in data. We provide our experiment code and pre-trained models at https://github.com/AllanYangZhou/metalearning-symmetries. | Meta-Learning Symmetries by Reparameterization |
d3495200 | Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures. In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VARNAMING, in which a network attempts to predict the name of a variable given its usage, and VARMISUSE, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VARMISUSE task in many cases. Additionally, our testing showed that VARMISUSE identifies a number of bugs in mature open-source projects. | LEARNING TO REPRESENT PROGRAMS WITH GRAPHS |
d498451 | We present a novel layerwise optimization algorithm for the learning objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large class of convolutional neural networks. Specifically, PL-CNNs employ piecewise linear non-linearities such as the commonly used ReLU and max-pool, and an SVM classifier as the final layer. The key observation of our approach is that the problem corresponding to the parameter estimation of a layer can be formulated as a difference-of-convex (DC) program, which happens to be a latent structured SVM. We optimize the DC program using the concave-convex procedure, which requires us to iteratively solve a structured SVM problem. This allows to design an optimization algorithm with an optimal learning rate that does not require any tuning. Using the MNIST, CIFAR and ImageNet data sets, we show that our approach always improves over the state of the art variants of backpropagation and scales to large data and large network settings. | TRUSTING SVM FOR PIECEWISE LINEAR CNNS |
d209318411 | Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest. We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models based on randomized truncation of infinite series. If parameterized by an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator. We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost. This estimator also allows use of latent variable models for tasks where unbiased estimators, rather than marginal likelihood lower bounds, are preferred, such as minimizing reverse KL divergences and estimating score functions. * Equal contribution. | SUMO: UNBIASED ESTIMATION OF LOG MARGINAL PROBABILITY FOR LATENT VARIABLE MODELS |
d248085789 | Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulted from privacy constraints. Extensive attention has been paid to SSL approaches based on Siamese networks. However, such an effort has not yet revealed deep insights into various fundamental building blocks for the federated self-supervised learning (FedSSL) architecture. We aim to fill in this gap via in-depth empirical study and propose a new method to tackle the nonindependently and identically distributed (non-IID) data problem of decentralized data. Firstly, we introduce a generalized FedSSL framework that embraces existing SSL methods based on Siamese networks and presents flexibility catering to future methods. In this framework, a server coordinates multiple clients to conduct SSL training and periodically updates local models of clients with the aggregated global model. Using the framework, our study uncovers unique insights of FedSSL: 1) stop-gradient operation, previously reported to be essential, is not always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL is particularly beneficial for non-IID data. Inspired by the insights, we then propose a new approach for model update, Federated Divergence-aware Exponential Moving Average update (FedEMA). FedEMA updates local models of clients adaptively using EMA of the global model, where the decay rate is dynamically measured by model divergence. Extensive experiments demonstrate that FedEMA outperforms existing methods by 3-4% on linear evaluation. We hope that this work will provide useful insights for future research. | DIVERGENCE-AWARE FEDERATED SELF-SUPERVISED LEARNING |
d21473141 | We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are its implications in terms of learning.This principle creates a powerful inductive bias that we leverage with neural architectures that are defined recursively and dynamically, by learning two scale-invariant atomic operations: how to split a given input into smaller sets, and how to merge two partially solved tasks into a larger partial solution. Our model can be trained in weakly supervised environments, namely by just observing input-output pairs, and in even weaker environments, using a non-differentiable reward signal. Moreover, thanks to the dynamic aspect of our architecture, we can incorporate the computational complexity as a regularization term that can be optimized by backpropagation. We demonstrate the flexibility and efficiency of the Divide-and-Conquer Network on three combinatorial and geometric tasks: sorting, clustering and convex hulls. Thanks to the dynamic programming nature of our model, we show significant improvements in terms of generalization error and computational complexity. arXiv:1611.02401v5 [cs.LG] 31 May 2017 mathematics, from geometry to graph theory. In the case of images and audio signals, invariance principles are also critical for success: CNNs exploit both translation invariance and scale separation with multilayer, localized convolutional operators. In our scenario of discrete algorithmic tasks, we build our model on the principle of divide and conquer, which provides us with a form of parameter sharing across scales akin to that of CNNs across space or RNNs across time.Whereas CNN and RNN models define algorithms with linear complexity, attention mechanisms [2] generally correspond to quadratic complexity, with notable exceptions [1]. This can result in a mismatch between the intrinsic complexity required to solve a given task and the complexity that is given to the neural network to solve it, which may impact its generalization performance. Our motivation is that learning cannot be 'complete' until these complexities match, and we start this quest by first focusing on problems for which the intrinsic complexity is well known and understood.Our Divide-and-Conquer Networks (DCN) contain two modules: a split phase that is applied recursively and dynamically to the input in a coarse-to-fine way to create a hierarchical partition encoded as a binary tree; and a merge phase that traces back that binary tree in a fine-to-coarse way by progressively combining partial solutions; seeFigure 1. Each of these phases is parametrized by a single neural network that is applied recursively at each node of the tree, enabling parameter sharing across different scales and leading to good sample complexity and generalisation.In this paper, we attempt to incorporate the scale-invariance prior with the desiderata to only require weak supervision. In particular, we consider two setups: learning from input-output pairs, and learning from a non-differentiable reward signal. Since our split block is inherently discrete, we resort to policy gradient to train the split parameters, while using standard backpropagation for the merge phase; see Section 5. An important benefit of our framework is that the architecture is dynamically determined, which suggests using the computational complexity as a regularization term. As shown in the experiments, computational complexity is a good proxy for generalisation error in the context of discrete algorithmic tasks. We demonstrate our model on algorithmic and geometric tasks with some degree of scale self-similarity: sorting, clustering and planar convex-hull. Our numerical results on these tasks reaffirm the fact that whenever the problem has scale invariance, then exploiting it leads to improved generalization and computational complexity over previous approaches.Related WorkUsing neural networks to solve algorithmic tasks is an active area of current research, but its models can be traced back to context free grammars[5]. In particular, dynamic learning appears in works such as[11]and[15]. The current research in the area is dominated by RNNs [9,7], LSTMs [8], sequence-to-sequence neural models[14,19], attention mechanisms 2 | Divide and Conquer Networks |
d52895739 | To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping. Despite its empirical success, little is understood about why the straight-through gradient method works.Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov's dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant, that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method. ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness. For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization. For binary quantization, our analysis shows both theoretically and experimentally that ProxQuant is more stable than the straight-through gradient method (i.e. BinaryConnect), challenging the indispensability of the straight-through gradient method and providing a powerful alternative. | ProxQuant: Quantized Neural Networks via Proximal Operators |
d56177829 | Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches. | INFORMATION-DIRECTED EXPLORATION FOR DEEP REINFORCEMENT LEARNING |
d246634839 | Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non-stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common solution when learning in changing environments is to discard/downweight past data; however, this simple mechanism of "forgetting" fails to account for the fact that many real-world environments involve revisiting similar states. We propose a new framework, Bayes with Adaptive Memory (BAM), that takes advantage of past experience by allowing the agent to choose which past observations to remember and which to forget. We demonstrate that BAM generalizes many popular Bayesian update rules for non-stationary environments. Through a variety of experiments, we demonstrate the ability of BAM to continuously adapt in an ever-changing world. | BAM: BAYES WITH ADAPTIVE MEMORY |
d261076491 | Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English, leaving other languages largely behind. Building a competitive counterpart in other languages is highly challenging due to the low-resource nature of non-English multimodal data (i.e., lack of large-scale, high-quality image-text data). In this work, we propose MPM, an effective training paradigm for training large multimodal models in low-resource languages. MPM demonstrates that Multilingual language models can Pivot zero-shot Multimodal learning across languages. Specifically, based on a strong multilingual large language model, multimodal models pretrained on English-only image-text data can well generalize to other languages in a zero-shot manner for both image-to-text and text-to-image generation, even surpassing models trained on image-text data in native languages. Taking Chinese as a practice of MPM, we build large multimodal models VISCPM in image-to-text and text-to-image generation, which achieve state-of-the-art (opensource) performance in Chinese. To facilitate future research, we open-source codes and model weights at https://github.com/OpenBMB/VisCPM. | Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages |
d257219618 | There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generationbased zero-shot learning has achieved impressive success. In this paradigm, the synthesized data from the PLM acts as the carrier of knowledge, which is used to train a task-specific model with orders of magnitude fewer parameters than the PLM, achieving both higher performance and efficiency than prompt-based zero-shot learning methods on PLMs. The main hurdle of this approach is that the synthesized data from PLM usually contains a significant portion of low-quality samples. Fitting on such data will greatly hamper the performance of the taskspecific model, making it unreliable for deployment. Previous methods remedy this issue mainly by filtering synthetic data using heuristic metrics(e.g., output confidence), or refining the data with the help of a human expert, which comes with excessive manual tuning or expensive costs. In this paper, we propose a novel noise-robust re-weighting framework SUNGEN to automatically construct high-quality data for zero-shot classification problems. Our framework features the ability to learn the sample weights indicating data quality without requiring any human annotation. We theoretically and empirically verify the ability of our method to help construct good-quality synthetic datasets. Notably, SUNGEN-LSTM yields a 9.8% relative improvement than the baseline on average accuracy across eight different established text classification tasks. collaboration for natural language inference dataset creation. arXiv preprint arXiv:2201.05955, 2022. . Training gaussian mixture models at scale via coresets. | SELF-GUIDED NOISE-FREE DATA GENERATION FOR EFFICIENT ZERO-SHOT LEARNING |
d202565422 | Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only selfsupervision, reaching novel goals in cluttered scenes with unseen objects. However, due to the compounding uncertainty in long horizon video prediction and poor scalability of sampling-based planning optimizers, one significant limitation of these approaches is the ability to plan over long horizons to reach distant goals.To that end, we propose a framework for subgoal generation and planning, hierarchical visual foresight (HVF), which generates subgoal images conditioned on a goal image, and uses them for planning. The subgoal images are directly optimized to decompose the task into easy to plan segments, and as a result, we observe that the method naturally identifies semantically meaningful states as subgoals. Across three out of four simulated vision-based manipulation tasks, we find that our method achieves nearly a 200% performance improvement over planning without subgoals and model-free RL approaches. Further, our experiments illustrate that our approach extends to real, cluttered visual scenes. † Work completed at Google Brain Videos and code are available at: | HIERARCHICAL FORESIGHT: SELF-SUPERVISED LEARNING OF LONG-HORIZON TASKS VIA VISUAL SUBGOAL GENERATION |
d240354511 | Recently, self-supervised learning has attracted great attention, since it only requires unlabeled data for model training. Contrastive learning is one popular method for self-supervised learning and has achieved promising empirical performance. However, the theoretical understanding of its generalization ability is still limited. To this end, we define a kind of (σ, δ)-measure to mathematically quantify the data augmentation, and then provide an upper bound of the downstream classification error rate based on the measure. It reveals that the generalization ability of contrastive self-supervised learning is related to three key factors: alignment of positive samples, divergence of class centers, and concentration of augmented data. The first two factors are properties of learned representations, while the third one is determined by pre-defined data augmentation. We further investigate two canonical contrastive losses, InfoNCE and cross-correlation, to show how they provably achieve the first two factors. Moreover, we conduct experiments to study the third factor, and observe a strong correlation between downstream performance and the concentration of augmented data. * Equal contribution (α-β ordering). Correspondence to Weiran Huang (weiran.huang@outlook.com). | TOWARDS THE GENERALIZATION OF CONTRASTIVE SELF-SUPERVISED LEARNING |
d85504763 | Mathematical reasoning-a core ability within human intelligence-presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primarily on the back of experience and evidence, but on the basis of inferring, learning, and exploiting laws, axioms, and symbol manipulation rules. In this paper, we present a new challenge for the evaluation (and eventually the design) of neural architectures and similar system, developing a task suite of mathematics problems involving sequential questions and answers in a free-form textual input/output format. The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes. Having described the data generation process and its potential future expansions, we conduct a comprehensive analysis of models from two broad classes of the most powerful sequence-to-sequence architectures and find notable differences in their ability to resolve mathematical problems and generalize their knowledge. | ANALYSING MATHEMATICAL REASONING ABILITIES OF NEURAL MODELS |
d237532482 | Estimating the performance of a machine learning system is a longstanding challenge in artificial intelligence research.Today, this challenge is especially relevant given the emergence of systems which appear to increasingly outperform human beings.In some cases, this "superhuman" performance is readily demonstrated; for example by defeating legendary human players in traditional two player games.On the other hand, it can be challenging to evaluate classification models that potentially surpass human performance.Indeed, human annotations are often treated as a ground truth, which implicitly assumes the superiority of the human over any models trained on human annotations.In reality, human annotators can make mistakes and be subjective.Evaluating the performance with respect to a genuine oracle may be more objective and reliable, even when querying the oracle is expensive or impossible.In this paper, we first raise the challenge of evaluating the performance of both humans and models with respect to an oracle which is unobserved.We develop a theory for estimating the accuracy compared to the oracle, using only imperfect human annotations for reference.Our analysis provides a simple recipe for detecting and certifying superhuman performance in this setting, which we believe will assist in understanding the stage of current research on classification.We validate the convergence of the bounds and the assumptions of our theory on carefully designed toy experiments with known oracles.Moreover, we demonstrate the utility of our theory by meta-analyzing large-scale natural language processing tasks, for which an oracle does not exist, and show that under our assumptions a number of models from recent years are with high probability superhuman. | Humanly Certifying Superhuman Classifiers |
d253107515 | The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchronization of client models where the speed of synchronization depends upon the slowest client. In this work, we propose SWIFT: a novel wait-free decentralized FL algorithm that allows clients to conduct training at their own speed. Theoretically, we prove that SWIFT matches the gold-standard iteration convergence rate O(1/ √ T ) of parallel stochastic gradient descent for convex and non-convex smooth optimization (total iterations T ). Furthermore, we provide theoretical results for IID and non-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms. Although SWIFT achieves the same iteration convergence rate with respect to T as other state-of-the-art (SOTA) parallel stochastic algorithms, it converges faster with respect to run-time due to its wait-free structure. Our experimental results demonstrate that SWIFT's run-time is reduced due to a large reduction in communication time per epoch, which falls by an order of magnitude compared to synchronous counterparts. Furthermore, SWIFT produces loss levels for image classification, over IID and non-IID data settings, upwards of 50% faster than existing SOTA algorithms. Federated Learning (FL) is an increasingly popular setting to train powerful deep neural networks with data derived from an assortment of clients. Recent research (Lian et al., 2017; Li et al., 2019; Wang & Joshi, 2018) has focused on constructing decentralized FL algorithms that overcome speed and scalability issues found within classical centralized FL (McMahan et al., 2017; Savazzi et al., 2020). While decentralized algorithms have eliminated a major bottleneck in the distributed setting, the central server, their scalability potential is still largely untapped. Many are plagued by high communication time per round (Wang et al., 2019). Shortening the communication time per round allows more clients to connect and then communicate with one another, thereby increasing scalability.Due to the synchronous nature of current decentralized FL algorithms, communication time per round, and consequently run-time, is amplified by parallelization delays. These delays are caused by the slowest client in the network. To circumvent these issues, asynchronous decentralized FL algorithms have been proposedLuo et al., 2020;Liu et al., 2022;Nadiradze et al., 2021). However, these algorithms still suffer from high communication time per round. Furthermore, their communication protocols either do not propagate models well throughout the network (via gossip algorithms) or require partial synchronization. Finally, these asynchronous algorithms rely on a deterministic bounded-delay assumption, which ensures that the slowest client in the network updates at least every τ iterations. This assumption is satisfied only under certain conditions(Abbasloo & Chao, 2020), and worsens the convergence rate by adding a sub-optimal reliance on τ .To remedy these drawbacks, we propose the Shared WaIt-Free Transmission (SWIFT) algorithm: an efficient, scalable, and high-performing decentralized FL algorithm. Unlike other decentralized 1 | SWIFT: RAPID DECENTRALIZED FEDERATED LEARNING VIA WAIT-FREE MODEL COMMUNICATION |
d252596112 | A Transformer-based deep direct sampling method is proposed for electrical impedance tomography, a well-known severely ill-posed nonlinear boundary value inverse problem. A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images. An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks? Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels. Then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism. The new method achieves superior accuracy over its predecessors and contemporary operator learners and shows robustness to noises in benchmarks. This research shall strengthen the insights that, despite being invented for natural language processing tasks, the attention mechanism offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures. . The factorization method for electrical impedance tomography data from a new planar device. International journal of biomedical imaging, 2007: 83016-83016, 2007. URL https://pubmed.ncbi.nlm.nih.gov/ . | TRANSFORMER MEETS BOUNDARY VALUE INVERSE PROBLEMS |
d239016684 | We provide nearly optimal algorithms for online facility location (OFL) with predictions. In OFL, n demand points arrive in order and the algorithm must irrevocably assign each demand point to an open facility upon its arrival. The objective is to minimize the total connection costs from demand points to assigned facilities plus the facility opening cost. We further assume the algorithm is additionally given for each demand point x i a natural prediction f pred xi , which is supposed to be the facility f opt xi that serves x i in the offline optimal solution. Our main result is an O(min{log nη∞ OPT , log n})-competitive algorithm where η ∞ is the maximum prediction error (i.e., the distance between f pred xi and f opt xi ). Our algorithm overcomes the fundamental Ω( log n log log n ) lower bound of OFL (without predictions) when η ∞ is small, and it still maintains O(log n) ratio even when η ∞ is unbounded. Furthermore, our theoretical analysis is supported by empirical evaluations for the tradeoffs between η ∞ and the competitive ratio on various real datasets of different types. | ONLINE FACILITY LOCATION WITH PREDICTIONS |
d259165262 | Recent deep neural networks (DNNs) have come to rely on vast amounts of training data, providing an opportunity for malicious attackers to exploit and contaminate the data to carry out backdoor attacks. These attacks significantly undermine the reliability of DNNs. However, existing backdoor attack methods make unrealistic assumptions, assuming that all training data comes from a single source and that attackers have full access to the training data. In this paper, we address this limitation by introducing a more realistic attack scenario where victims collect data from multiple sources, and attackers cannot access the complete training data. We refer to this scenario as data-constrained backdoor attacks. In such cases, previous attack methods suffer from severe efficiency degradation due to the entanglement between benign and poisoning features during the backdoor injection process.IntroductionDeep neural networks (DNNs) are widely utilized and powerful machine learning algorithms inspired by the structure and functioning of the human brain. They excel at learning intricate patterns in data, making them invaluable for various applications such as image recognition[17,21], natural language processing[33,68], image generation[20,30], and anomaly detection[45,64]. However, the effectiveness of DNNs heavily relies on the quantity and quality of the training data. For instance, Stable Diffusion [49], a generative model with 983 million parameters, owes its success in image generation tasks to pre-training on 5 billion image-text pairs. Similarly, GPT-3 [3], a language model with 175 billion * Equal Contribution. | Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios |
d256389660 | We study a security threat to adversarial multi-armed bandits, in which an attacker perturbs the loss or reward signal to control the behavior of the victim bandit player. We show that the attacker is able to mislead any no-regret adversarial bandit algorithm into selecting a suboptimal target arm in every but sublinear (T − o(T )) number of rounds, while incurring only sublinear (o(T )) cumulative attack cost. This result implies critical security concern in real-world bandit-based systems, e.g., in online recommendation, an attacker might be able to hijack the recommender system and promote a desired product. Our proposed attack algorithms require knowledge of only the regret rate, thus are agnostic to the concrete bandit algorithm employed by the victim player. We also derived a theoretical lower bound on the cumulative attack cost that any victim-agnostic attack algorithm must incur. The lower bound matches the upper bound achieved by our attack, which shows that our attack is asymptotically optimal. | ADVERSARIAL ATTACKS ON ADVERSARIAL BANDITS |
d52086172 | We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian nonparametric version of the variational autoencoder that has a latent representation with stochastic dimensionality. We experimentally demonstrate that the SB-VAE, and a semisupervised variant, learn highly discriminative latent representations that often outperform the Gaussian VAE's. | STICK-BREAKING VARIATIONAL AUTOENCODERS |
d257079127 | Human motion transfer aims to transfer motions from a target dynamic person to a source static one for motion synthesis. An accurate matching between the source person and the target motion in both large and subtle motion changes is vital for improving the transferred motion quality. In this paper, we propose Human MotionFormer, a hierarchical ViT framework that leverages global and local perceptions to capture large and subtle motion matching, respectively. It consists of two ViT encoders to extract input features (i.e., a target motion image and a source human image) and a ViT decoder with several cascaded blocks for feature matching and motion transfer. In each block, we set the target motion feature as Query and the source person as Key and Value, calculating the crossattention maps to conduct a global feature matching. Further, we introduce a convolutional layer to improve the local perception after the global cross-attention computations. This matching process is implemented in both warping and generation branches to guide the motion transfer. During training, we propose a mutual learning loss to enable the co-supervision between warping and generation branches for better motion representations. Experiments show that our Human MotionFormer sets the new state-of-the-art performance both qualitatively and quantitatively. Project page: https://github.com/KumapowerLIU/ Human-MotionFormer * X. Han and H. Liu contribute equally. † Y. Song and Q. Chen are the corresponding authors. | HUMAN MOTIONFORMER: TRANSFERRING HUMAN MOTIONS WITH VISION TRANSFORMERS |
d245144606 | Neural memory enables fast adaptation to new tasks with just a few training samples. Existing memory models store features only from the single last layer, which does not generalize well in presence of a domain shift between training and test distributions. Rather than relying on a flat memory, we propose a hierarchical alternative that stores features at different semantic levels. We introduce a hierarchical prototype model, where each level of the prototype fetches corresponding information from the hierarchical memory. The model is endowed with the ability to flexibly rely on features at different semantic levels if the domain shift circumstances so demand. We meta-learn the model by a newly derived hierarchical variational inference framework, where hierarchical memory and prototypes are jointly optimized. To explore and exploit the importance of different semantic levels, we further propose to learn the weights associated with the prototype at each level in a data-driven way, which enables the model to adaptively choose the most generalizable features. We conduct thorough ablation studies to demonstrate the effectiveness of each component in our model. The new state-of-the-art performance on cross-domain and competitive performance on traditional few-shot classification further substantiates the benefit of hierarchical variational memory. | HIERARCHICAL VARIATIONAL MEMORY FOR FEW-SHOT LEARNING ACROSS DOMAINS |
d259129342 | Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (e.g. commonsense knowledge). In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs). Specifically, we formulate a novel task CORR2CAUSE, which takes a (set of) correlational statements and determines the causal relationship between the variables. We curate a large-scale dataset of more than 400K samples, on which we evaluate seventeen existing LLMs. Through our experiments, we identify a key shortcoming of LLMs in terms of their causal inference skills, and show that these models achieve almost close to random performance on the task. This shortcoming is somewhat mitigated when we try to re-purpose LLMs for this skill via finetuning, but we find that these models still fail to generalize -they can only perform causal inference in in-distribution settings when variable names and textual expressions used in the queries are similar to those in the training set, but fail in out-of-distribution settings generated by perturbing these queries. CORR2CAUSE is a challenging task for LLMs, and would be helpful in guiding future research on improving LLMs' pure reasoning skills and generalizability. 1 * This work originated during Zhijing's internship at Meta AI. | Can Large Language Models Infer Causation from Correlation? |
d52901998 | For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence. In real-world scenarios, future states become increasingly uncertain and multi-modal, particularly on long time horizons. Dropout based Bayesian inference provides a computationally tractable, theoretically well grounded approach to learn likely hypotheses/models to deal with uncertain futures and make predictions that correspond well to observationsare well calibrated. However, it turns out that such approaches fall short to capture complex real-world scenes, even falling behind in accuracy when compared to the plain deterministic approaches. This is because the used log-likelihood estimate discourages diversity. In this work, we propose a novel Bayesian formulation for anticipating future scene states which leverages synthetic likelihoods that encourage the learning of diverse models to accurately capture the multi-modal nature of future scene states. We show that our approach achieves accurate state-of-the-art predictions and calibrated probabilities through extensive experiments for scene anticipation on Cityscapes dataset. Moreover, we show that our approach generalizes across diverse tasks such as digit generation and precipitation forecasting. | BAYESIAN PREDICTION OF FUTURE STREET SCENES USING SYNTHETIC LIKELIHOODS |
d235313715 | Potential games are arguably one of the most important and widely studied classes of normal form games. They define the archetypal setting of multi-agent coordination as all agent utilities are perfectly aligned with each other via a common potential function. Can this intuitive framework be transplanted in the setting of Markov Games? What are the similarities and differences between multi-agent coordination with and without state dependence? We present a novel definition of Markov Potential Games (MPG) that generalizes prior attempts at capturing complex stateful multi-agent coordination. Counter-intuitively, insights from normal-form potential games do not carry over as MPGs can consist of settings where state-games can be zero-sum games. In the opposite direction, Markov games where every state-game is a potential game are not necessarily MPGs. Nevertheless, MPGs showcase standard desirable properties such as the existence of deterministic Nash policies. In our main technical result, we prove (polynomially fast in the approximation error) convergence of independent policy gradient to Nash policies by adapting recent gradient dominance property arguments developed for single agent MDPs to multi-agent learning settings. arXiv:2106.01969v3 [cs.LG] 28 Sep 2021 policies (π * 1 , ..., π * n ) so that by fixing the stationary policies of all agents but i, π * i is an optimal policy for the resulting single-agent MDP and this is true for all 1 ≤ i ≤ n 1 (see Definition 1). Note that in multi-agent settings, Nash policies may not be unique in principle.A common approach for computing Nash policies in MDPs is the use of policy gradient methods. There has been significant progress in the analysis of policy gradient methods during the last couple of years, notably including the works of [1] (and references therein), but it has mainly concerned the single-agent case: the convergence properties of policy gradient in MARL remain poorly understood. Existing steps towards a theory for multi-agent settings involve the papers of [11] who show convergence of independent policy gradient to the optimal policy for two-agent zero-sum stochastic games, of [37] who improve the result of [11] using optimistic policy gradient and of [39] who study extensions of Natural Policy Gradient using function approximation. It is worth noting that the positive results of[11,37] and [39] depend on the fact that two-agent stochastic zero-sum games satisfy the "min-max equals max-min" property [28] (even though the value-function landscape may not be convex-concave, which implies that Von Neumann's celebrated minimax theorem may not be applicable).Model and Informal Statement of Results.While the previous works enhance our understanding in competitive interactions, i.e., interactions in which gains can only come at the expense of others, MARL in cooperative settings remains largely under-explored and constitutes one of the current frontiers in AI research[10,9]. Based on the above, our work is motivated by the following natural question:Can we get (provable) convergence guarantees for multi-agent RL settings in which cooperation is desirable?To address this question, we define and study a class of n-agent MDPs that naturally generalize normal form potential games[22], called Markov Potential Games (MPGs). In words, a multi-agent MDP is a MPG as long as there exists a (state-dependent) real-valued potential function Φ so that if an agent i changes their policy (and the rest of the agents keep their policy unchanged), the difference in agent i's value/utility, V i , is captured by the difference in the value of Φ (see Definition 2). Weighted and ordinal MPGs are defined similar to their normal form counterparts (see Remark 1).Under our definition, we answer the above motivating question in the affirmative. In particular, we show that if every agent i independently runs (with simultaneous updates) policy gradient on his utility/value V i , then, after O(1/ 2 ) iterations, the system will reach an -approximate Nash policy (see informal Theorem 1.1 and formal Theorem 4.5). Moreover, for the finite sample analogue, i.e., if every agent i independently runs (with simultaneous updates) stochastic policy gradient, we show that the system will reach an -approximate Nash policy after O(1/ 6 ) iterations.Along the way, we prove several properties about the structure of MPGs and their Nash policies (see Theorem 1.2 and Section 3). In sum, our results can be summarized in the following two Theorems.Theorem 1.1 (Convergence of Policy Gradient (Informal)). Consider a MPG with n agents and let > 0. Suppose that each agent i runs independent policy gradient using direct 1 Analogue of Nash equilibrium notion.2 parameterization on their policy and that the updates are simultaneous. Then, the learning dynamics reach an -Nash policy after O(1/ 2 ) iterations. If instead, each agent i runs stochastic policy gradient using greedy parameterization (see (4)) on his policy and that the updates are simultaneous, then the learning dynamics reach an -Nash policy after O(1/ 6 ) iterations.This result holds trivially for weighted MPGs and asymptotically also for ordinal MPGs, see Remark 5. Theorem 1.2 (Structural Properties of MPGs). The following facts are true for MPGs with n-agents: a. There always exists a Nash policy profile (π * 1 , . . . , π * n ) so that π * i is deterministic for each agent i (see Theorem 3.1). b. We can construct MDPs for which each state is a (normal-form) potential game but which are not MPGs. This can be true regardless of whether the whole MDP is competitive or cooperative in nature (see Examples 1 and 2, respectively). On the opposite side, we can construct MDPs that are MPGs but which include states that are purely competitive (i.e., zero-sum games), see Example 3.c. We provide sufficient conditions so that a MDP is a MPG. These include cases where each state is a (normal-form) potential game and the transition probabilities are not affected by agents actions or the reward functions satisfy certain regularity conditions between different states (see conditions C1 and C2 in Proposition 3.2).Technical Overview. The first challenge in the proof of Theorem 1.1 is that multi-agent settings (MPGs) do not satisfy the gradient dominance property, which is an important part in the proof of convergence of policy gradient in single-agent settings [1]. In particular, different Nash policies may yield different value to each agent and as a result, there is not a properly defined notion of value in MPGs (in contrast to zero-sum stochastic games[11]). On the positive side, we show that agent-wise (i.e., after fixing the policy of all agents but i), the value function, V i , satisfies the gradient dominance property along the direction of π i (policy of agent i). This can be leveraged to show that every (approximate) stationary point (Definition 4) of the potential function Φ is an (approximate) Nash policy (Lemma 4.2). As a result, convergence to an approximate Nash policy is established by first showing that Φ is smooth and then by applying Projected Gradient Ascent (PGA) on Φ. This step uses the rather well-known fact that (PGA) converges to -stationary points in O(1/ 2 ) iterations for smooth functions. As a result, by applying PGA on the potential Φ, one gets an approximate Nash policy. Our convergence result then follows by showing that PGA on the potential function, Φ, generates the same dynamics as if each agent i runs independent PGA on their value function, V i . In the case that agents do not have access to exact gradients, we derive a similar result for finite samples. In this case, we apply Projected Stochastic Gradient Ascent (PSGA) on Φ which (as was the case for PGA) can be shown to be the same as when agents apply PSGA independently on their individual value functions. The key is to get an unbiased sample for the gradient of the value functions and prove that it has bounded variance (in terms of the parameters of the MPG). This comes from the discount factor, γ; in this case, 1 − γ can be interpreted as the probability to terminate the MDP at a particular state (and γ to continue). This can be used to show that a trajectory of the MDP is an unbiased sample for the gradient | Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games |
d237532682 | We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of model size follows a certain scaling law. Specifically (i) We propose a formula which describes the scaling behavior of cross-entropy loss as a bivariate function of encoder and decoder size, and show that it gives accurate predictions under a variety of scaling approaches and languages; we show that the total number of parameters alone is not sufficient for such purposes. (ii) We observe different power law exponents when scaling the decoder vs scaling the encoder, and provide recommendations for optimal allocation of encoder/decoder capacity based on this observation. (iii) We also report that the scaling behavior of the model is acutely influenced by composition bias of the train/test sets, which we define as any deviation from naturally generated text (either via machine generated or human translated text). We observe that natural text on the target side enjoys scaling, which manifests as successful reduction of the cross-entropy loss. (iv) Finally, we investigate the relationship between the cross-entropy loss and the quality of the generated translations. We find two different behaviors, depending on the nature of the test data. For test sets which were originally translated from target language to source language, both loss and BLEU score improve as model size increases. In contrast, for test sets originally translated from source language to target language, the loss improves, but the BLEU score stops improving after a certain threshold. We release generated text from all models used in this study.Preprint. Under review. | Scaling Laws for Neural Machine Translation |
d3303573 | Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL). While current trust region strategies are effective for continuous control, they typically require a prohibitively large amount of on-policy interaction with the environment. To address this problem, we propose an offpolicy trust region method, Trust-PCL. The algorithm is the result of observing that the optimal policy and state values of a maximum reward objective with a relativeentropy regularizer satisfy a set of multi-step pathwise consistencies along any path. Thus, Trust-PCL is able to maintain optimization stability while exploiting off-policy data to improve sample efficiency. When evaluated on a number of continuous control tasks, Trust-PCL improves the solution quality and sample efficiency of TRPO. 1 * | Trust-PCL: An Off-Policy Trust Region Method for Continuous Control |
d256697500 | Generalizable manipulation skills, which can be composed to tackle longhorizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks, mostly composed of a suite of simulatable environments, are insufficient to push cutting-edge research works because they lack object-level topological and geometric variations, are not based on fully dynamic simulation, or are short of native support for multiple types of manipulation tasks. To this end, we present ManiSkill2, the next generation of the SAPIEN ManiSkill benchmark, to address critical pain points often encountered by researchers when using benchmarks for generalizable manipulation skills. ManiSkill2 includes 20 manipulation task families with 2000+ object models and 4M+ demonstration frames, which cover stationary/mobile-base, single/dualarm, and rigid/soft-body manipulation tasks with 2D/3D-input data simulated by fully dynamic engines. It defines a unified interface and evaluation protocol to support a wide range of algorithms (e.g., classic sense-plan-act, RL, IL), visual observations (point cloud, RGBD), and controllers (e.g., action type and parameterization). Moreover, it empowers fast visual input learning algorithms so that a CNN-based policy can collect samples at about 2000 FPS with 1 GPU and 16 processes on a regular workstation. It implements a render server infrastructure to allow sharing rendering resources across all environments, thereby significantly reducing memory usage. We open-source all codes of our benchmark (simulator, environments, and baselines) and host an online challenge open to interdisciplinary researchers.Figure 1: ManiSkill2 provides a unified, fast, and accessible system that encompasses well-curated manipulation tasks (e.g., stationary/mobile-base, single/dual-arm, rigid/soft-body). | MANISKILL2: A UNIFIED BENCHMARK FOR GENERALIZABLE MANIPULATION SKILLS |
d12256925 | We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset. Alice will "propose" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively. Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent. When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward. | Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play |
d108367114 | The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e.g. input-output behavior. Many current approaches achieve impressive results after training on randomly generated I/O examples in limited domain-specific languages (DSLs), as with string transformations in RobustFill. However, we empirically discover that applying test input generation techniques for languages with control flow and rich input space causes deep networks to generalize poorly to certain data distributions; to correct this, we propose a new methodology for controlling and evaluating the bias of synthetic data distributions over both programs and specifications. We demonstrate, using the Karel DSL and a small Calculator DSL, that training deep networks on these distributions leads to improved cross-distribution generalization performance. * | SYNTHETIC DATASETS FOR NEURAL PROGRAM SYNTHESIS |
d222140630 | This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. This perspective leads to a machine learning framework in which seemingly unrelated tasks can be solved by a single model, by embedding their input and output variables into a shared space. An implementation of the framework is developed in which these variable embeddings are learned jointly with internal model parameters. In experiments, the approach is shown to (1) recover intuitive locations of variables in space and time, (2) exploit regularities across related datasets with completely disjoint input and output spaces, and (3) exploit regularities across seemingly unrelated tasks, outperforming task-specific single-task models and multi-task learning alternatives. The results suggest that even seemingly unrelated tasks may originate from similar underlying processes, a fact that the traveling observer model can use to make better predictions. arXiv:2010.02354v1 [cs.NE] 5 Oct 2020 Preprint. Work in progress.(a) Intra-domain (b) Task Embeddings (c) Cross-domain (d) Variable Embeddings (TOM)yt = gt(f (xt))ŷt = g(f (xt, zt)))ŷt = gt(ft(xt))ŷj = gx i ∈x t f (xi, zi), zj | THE TRAVELING OBSERVER MODEL: MULTI-TASK LEARNING THROUGH SPATIAL VARIABLE EMBEDDINGS |
d210903109 | We consider the problem of learning control policies that optimize a reward function while satisfying constraints due to considerations of safety, fairness, or other costs. We propose a new algorithm, Projection-Based Constrained Policy Optimization (PCPO). This is an iterative method for optimizing policies in a two-step process: the first step performs a local reward improvement update, while the second step reconciles any constraint violation by projecting the policy back onto the constraint set. We theoretically analyze PCPO and provide a lower bound on reward improvement, and an upper bound on constraint violation, for each policy update. We further characterize the convergence of PCPO based on two different metrics: L 2 norm and Kullback-Leibler divergence. Our empirical results over several control tasks demonstrate that PCPO achieves superior performance, averaging more than 3.5 times less constraint violation and around 15% higher reward compared to state-of-the-art methods. 1 | PROJECTION-BASED CONSTRAINED POLICY OPTIMIZATION |
d252683127 | Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in Koh and Liang [2017]. They have been used for detecting data poisoning, detecting helpful and harmful examples, influence of groups of datapoints, etc. Recently, Ilyas et al. [2022] introduced a linear regression method they termed datamodels to predict the effect of training points on outputs on test data. The current paper seeks to provide a better theoretical understanding of such interesting empirical phenomena. The primary tool is harmonic analysis and the idea of noise stability. Contributions include: (a) Exact characterization of the learnt datamodel in terms of Fourier coefficients. (b) An efficient method to estimate the residual error and quality of the optimum linear datamodel without having to train the datamodel. (c) New insights into when influences of groups of datapoints may or may not add up linearly. arXiv:2210.01072v1 [cs.LG] 3 Oct 2022 1. We give reasons for existence of datamodels of Ilyas et al. [2022], the phenomenon that functions related to test error are well-approximable by a linear function θ 0 + i θ i x i . See Section 3.1.2. Section 2 gives exact characterizations of the θ i 's for data models with/without regularization. (Earlier, Ilyas et al. [2022] noted this for a special case: p = 0.5, 2 regularization) 3. Using our framework, we give a new algorithm to estimate the degree to which a test function f is well-approximated by a linear datamodel, without having to train the datamodel per se. See Section 3.2, where our method needs only O(1/ 3 ) samples instead of O(N/ 2 ).4. We study group influence, which quantifies the effect of adding or deleting a set I of datapoints to x. Ilyas et al. [2022] note that this can often be well-approximated by linearly adding the individual influences of points in I. Section 4 clarifies simple settings where linearity would fail, by a factor exponentially large in |I|, and also discusses potential reasons for the observed linearity. | Understanding Influence Functions and Datamodels via Harmonic Analysis |
d3517431 | The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular FP16 accumulating into FP32 . On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training, these works either present results for non-SOTA networks (for instance only AlexNet for ImageNet-1K), or relatively small datasets (like CIFAR-10). In this work, we train state-of-the-art visual understanding neural networks on ImageNet-1K dataset, with Integer operations on General Purpose (GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate (FMA) operations which take two pairs of INT16 operands and accumulate results into an INT32 output.We propose a shared exponent representation of tensors, and develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network operations. The nuances of developing an efficient integer convolution kernel is examined, including methods to handle overflow of the INT32 accumulator. We implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and these networks achieve or exceed SOTA accuracy within the same number of iterations as their FP32 counterparts without any change in hyper-parameters and with a 1.8X improvement in end-to-end training throughput. To the best of our knowledge these results represent the first INT16 training results on GP hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported accuracy using half precision representation.Published as a conference paper at ICLR 2018 fused-multiply and accumulate operations. For instance NVIDIA Volta NVIDIA (2017) provides 8X more half-precision Flops as compared to FP32. | MIXED PRECISION TRAINING OF CONVOLUTIONAL NEURAL NETWORKS USING INTEGER OPERATIONS |
d244117789 | In distribution compression, one aims to accurately summarize a probability distribution P using a small number of representative points. Near-optimal thinning procedures achieve this goal by sampling n points from a Markov chain and identifying √ n points with O(1/ √ n) discrepancy to P. Unfortunately, these algorithms suffer from quadratic or super-quadratic runtime in the sample size n. To address this deficiency, we introduce Compress++, a simple meta-procedure for speeding up any thinning algorithm while suffering at most a factor of 4 in error. When combined with the quadratic-time kernel halving and kernel thinning algorithms of Dwivedi and Mackey(2021), Compress++ delivers √ n points with O( log n/n) integration error and better-than-Monte-Carlo maximum mean discrepancy in O(n log 3 n) time and O( √ n log 2 n) space. Moreover, Compress++ enjoys the same near-linear runtime given any quadratic-time input and reduces the runtime of super-quadratic algorithms by a square-root factor. In our benchmarks with high-dimensional Monte Carlo samples and Markov chains targeting challenging differential equation posteriors, Compress++ matches or nearly matches the accuracy of its input algorithm in orders of magnitude less time.arXiv:2111.07941v6 [stat.ML] 18 Oct 2022Published as a conference paper at ICLR 2022Definition 2 (Sub-Gaussian thinning algorithm) For a function f , we call a thinning algorithm ALG f -sub-Gaussian with parameter ν and write ALG ∈ G f (ν) ifDef. 2 is equivalent to a sub-Gaussian tail bound for the integration error, and, by Boucheron et al. (2013, Section 2.3), if ALG ∈ G f (ν) then E[P SALG f | S in ] = P Sin f and, for all δ ∈ (0, 1), |P Sin f −P SALG f | ≤ ν(n) 2 log( 2 δ ), with probability at least 1 − δ given S in . | DISTRIBUTION COMPRESSION IN NEAR-LINEAR TIME |
d222381644 | Many recent methods for unsupervised representation learning involve training models to be invariant to different "views," or transformed versions of an input. However, designing these views requires considerable human expertise and experimentation, hindering widespread adoption of unsupervised representation learning methods across domains and modalities. To address this, we propose viewmaker networks: generative models that learn to produce input-dependent views for contrastive learning. We train this network jointly with an encoder network to produce adversarial p perturbations for an input, which yields challenging yet useful views without extensive human tuning. Our learned views, when applied to CIFAR-10, enable comparable transfer accuracy to the the well-studied augmentations used for the SimCLR model. Our views significantly outperforming baseline augmentations in speech (+9% absolute) and wearable sensor (+17% absolute) domains. We also show how viewmaker views can be combined with handcrafted views to improve robustness to common image corruptions. Our method demonstrates that learned views are a promising way to reduce the amount of expertise and effort needed for unsupervised learning, potentially extending its benefits to a much wider set of domains. Figure 1: Viewmaker networks generate complex and diverse input-dependent views for unsupervised learning. Examples shown are for CIFAR-10. Original image in center with pink border. | VIEWMAKER NETWORKS: LEARNING VIEWS FOR UNSUPERVISED REPRESENTATION LEARNING |
d263608162 | Prompt tuning in natural language processing (NLP) has become an increasingly popular method for adapting large language models to specific tasks.However, the transferability of these prompts, especially continuous prompts, between different models remains a challenge.In this work, we propose a zero-shot continuous prompt transfer method, where source prompts are encoded into relative space and the corresponding target prompts are searched for transferring to target models.Experimental results confirm the effectiveness of our method, showing that "task semantics" in continuous prompts can be generalized across various language models.Moreover, we find that combining "task semantics" from multiple source models can further enhance the generalizability of transfer. 1 | Zero-Shot Continuous Prompt Transfer: Generalizing Task Semantics Across Language Models |
d233004331 | Pooling is a critical operation in convolutional neural networks for increasing receptive fields and improving robustness to input variations. Most existing pooling operations downsample the feature maps, which is a lossy process. Moreover, they are not invertible: upsampling a downscaled feature map can not recover the lost information in the downsampling. By adopting the philosophy of the classical Lifting Scheme from signal processing, we propose LiftPool for bidirectional pooling layers, including LiftDownPool and LiftUpPool. LiftDownPool decomposes a feature map into various downsized sub-bands, each of which contains information with different frequencies. As the pooling function in LiftDownPool is perfectly invertible, by performing LiftDownPool backward, a corresponding up-pooling layer LiftUpPool is able to generate a refined upsampled feature map using the detail sub-bands, which is useful for image-to-image translation challenges. Experiments show the proposed methods achieve better results on image classification and semantic segmentation, using various backbones. Moreover, LiftDownPool offers better robustness to input corruptions and perturbations. arXiv:2104.00996v1 [cs.CV] 2 Apr 2021 Published as a conference paper at ICLR 2021 MaxPool LiftDownPool MaxUpPool LiftUpPool HL + + Original Max Indices Down-pool Up-pool LL LH HH | LIFTPOOL: BIDIRECTIONAL CONVNET POOLING |
d261100593 | In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) methods have been widely studied and demonstrate superior performance. However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities. Moreover, the fact that DR relies more on extrapolation will lead to suboptimal performance. To address the above limitations while retaining double robustness, we propose a stabilized doubly robust (StableDR) learning approach with a weaker reliance on extrapolation. Theoretical analysis shows that StableDR has bounded bias, variance, and generalization error bound simultaneously under inaccurate imputed errors and arbitrarily small propensities. In addition, we propose a novel learning approach for StableDR that updates the imputation, propensity, and prediction models cyclically, achieving more stable and accurate predictions. Extensive experiments show that our approaches significantly outperform the existing methods.Published as a conference paper at ICLR 2023 Imputation Model Prediction Model ℒ Propensity Model Prediction Model ℒ (a) Two-phase learning using single model. Propensity Model Imputation Model Prediction Model Stabilization ℒ ℒ ℒ Imputation Model Prediction Model ℒ (b) Doubly robust joint / double learning. (c) Proposed cycle learning with stabilization. | STABLEDR: STABILIZED DOUBLY ROBUST LEARNING FOR RECOMMENDATION ON DATA MISSING NOT AT RANDOM |
d228083457 | Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful image transformations (e.g., shift, zoom, color manipulations), and have similar interpretable effects across all categories that the GAN can generate. Some methods focus on user-specified transformations, while others discover transformations in an unsupervised manner. However, all existing techniques rely on an optimization procedure to expose those directions, and offer no control over the degree of allowed interaction between different transformations. In this paper, we show that "steering" trajectories can be computed in closed form directly from the generator's weights without any form of training or optimization. This applies to user-prescribed geometric transformations, as well as to unsupervised discovery of more complex effects. Our approach allows determining both linear and nonlinear trajectories, and has many advantages over previous methods. In particular, we can control whether one transformation is allowed to come on the expense of another (e.g., zoom-in with or without allowing translation to keep the object centered). Moreover, we can determine the natural end-point of the trajectory, which corresponds to the largest extent to which a transformation can be applied without incurring degradation. Finally, we show how transferring attributes between images can be achieved without optimization, even across different categories. | GAN "STEERABILITY" WITHOUT OPTIMIZATION |
d53941707 | We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning. A new experimental protocol is proposed that enforces typical constraints encountered in application scenarios. As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF. Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions. We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models. | A COMPREHENSIVE, APPLICATION-ORIENTED STUDY OF CATASTROPHIC FORGETTING IN DNNS |
d261276856 | Diffusion models have demonstrated impressive generative capabilities, but their exposure bias problem, described as the input mismatch between training and sampling, lacks in-depth exploration.In this paper, we systematically investigate the exposure bias problem in diffusion models by first analytically modelling the sampling distribution, based on which we then attribute the prediction error at each sampling step as the root cause of the exposure bias issue.Furthermore, we discuss potential solutions to this issue and propose an intuitive metric for it.Along with the elucidation of exposure bias, we propose a simple, yet effective, training-free method called Epsilon Scaling to alleviate the exposure bias.We show that Epsilon Scaling explicitly moves the sampling trajectory closer to the vector field learned in the training phase by scaling down the network output (Epsilon), mitigating the input mismatch between training and sampling.Experiments on various diffusion frameworks (ADM, DDPM/DDIM, EDM, LDM), unconditional and conditional settings, and deterministic vs. stochastic sampling verify the effectiveness of our method.Remarkably, our ADM-ES, as a SOTA stochastic sampler, obtains 2.17 FID on CIFAR-10 under 100-step unconditional generation.The code is available at https://github.com/forever208/ADM-ESand https://github.com/forever208/EDM-ESWe point out that the exposure bias problem in diffusion models lacks in-depth exploration.For example, there is no proper metric to quantify the exposure bias and no explicit error analysis for it.To shed light on exposure bias, we conduct a systematical investigation in this paper by first | ELUCIDATING THE EXPOSURE BIAS IN DIFFUSION MODELS |
d253265269 | Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples that are challenging for models, they do not explain why such mistakes arise. To address this need, we introduce ImageNet-X-a set of sixteen human annotations of factors such as pose, background, or lighting for the entire ImageNet-1k validation set as well as a random subset of 12k training images. Equipped with ImageNet-X, we investigate 2,200 current recognition models and study the types of mistakes as a function of model's (1) architecture -e.g. transformer vs. convolutional -, (2) learning paradigm -e.g. supervised vs. self-supervised -, and (3) training procedurese.g. data augmentation. Regardless of these choices, we find models have consistent failure modes across ImageNet-X categories. We also find that while data augmentation can improve robustness to certain factors, they induce spill-over effects to other factors. For example, color-jitter augmentation improves robustness to color and brightness, but surprisingly hurts robustness to pose. Together, these insights suggests that to advance the robustness of modern vision models, future research should focus on collecting additional diverse data and understanding data augmentation schemes. Along with these insights, we release a toolkit based on ImageNet-X to spur further study into the mistakes the image recognition systems make: https://facebookresearch.github.io/imagenetx/site/home. * hands-on and advising contributions 1 arXiv:2211.01866v1 [cs.CV] 3 Nov 2022 due to an unusual pose or an unseen color or dark lighting conditions. Researchers, instead, often measure robustness with respect to these examples' average accuracy. Average accuracy captures a model's mistakes, but does not reveal directions to reduce those mistakes. A hurdle to research progress is understanding not just that, but also why model failures occur.To meet this need, we introduce ImageNet-X, a set of human annotations pinpointing failure types for the popular ImageNet dataset. ImageNet-X labels distinguishing object factors such as pose, size, color, lighting, occlusions, co-occurences, and so on for each image in the validation set and a random subset of 12,000 training samples. Along with explaining how images in ImageNet vary, these annotations surface factors associated with models' mistakes (depicted inFigure 1).By analyzing the ImageNet-X labels, in section 2, we find that in ImageNet pose and background commonly vary, that classes can have distinct factors (such as dogs more often varying in pose compared to other classes), and that ImageNet's training and validation sets share similar distributions of factors. We then analyze, in section 3, the failure types of more than 2,200 models. We find that models, regardless of architecture, training dataset size, and even robustness interventions all share similar failure types in section 3.1. Additionally, differences in texture, subcategories (e.g., breeds), and occlusion are most associated with models' mistakes (SeeFigure 1and section 3.3.1). Among modeling choices such as architecture, supervision, data augmentations, and regularization methods, we find data augmentations can boost models' robustness. Common augmentations such as cropping and color-jittering however, can have unintended consequences by affecting unrelated factors (see section 3.3.2). For example, cropping improves robustness to pose and partial views, as expected, all the while affecting unrelated factors such as pattern, background, and texture. Together these findings suggests that to advance the robustness of modern vision models, future research should focus on improving training data -by collecting additional data and improving data augmentation schemes -and deemphasize the importance of other aspects such as choice of architecture and learning paradigm.We release all the ImageNet-X annotations along with an open-source toolkit to probe existing or new models' failure types. The data and code are available at https://facebookresearch.github.io/imagenetx/site/home.With ImageNet-X we equip the research community with a tool to pin-point a models' failure types. We hope this spurs new research directions to improve the reliability of deep learning vision systems.2 ImageNet-X: annotating ImageNet with variation labels ImageNet-X contains human annotations for each of the 50,000 images in the validation set of the ImageNet dataset and 12,000 random sample from the training set. Since it's difficult to annotate factors of variations by looking at a single image in isolation, we obtain the annotation by comparing a validation set image to the three class-prototypical images and ask the annotators to describe the image by contrasting it with the prototypical images. We define the prototypical images as the most likely images under ResNet-50 model 1[15]. Trained annotators select among sixteen distinguishing factors, possibly multiple, and write a text description as well as one-word summaries of key differences. The form is illustrated inFigure 1. The factors span pose, various forms of occlusion, styles, and include a subcategory factor capturing whether the image is of a distinct type or breed from the same class (full definitions in Appendix A.2). The text descriptions account for factors outside the sixteen we provide. After training the annotators and verifying | ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations |
d59553475 | We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search. Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with a kernel function defined over our proposed embedding space to select architectures for evaluation. We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and reinforcement learning(Ashok et al., 2018). The compressed architectures found by our method are also better than the state-of-the-art manually-designed compact architecture ShuffleNet(Zhang et al., 2018). We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training. * indicates equal contribution.Published as a conference paper at ICLR 2019 have multiple layers, multiple branches and multiple skip connections, defining an embedding space over all architectures is non-trivial. In this work, we propose a method for mapping a diverse range of discrete architectures to a continuous embedding space through the use of recurrent neural networks. The learned embedding space allows us to perform BO to efficiently search for compressed student architectures that are also expected to have high accuracy.We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and reinforcement learning(Ashok et al., 2018). For example, our search algorithm can compress VGG-19 (Simonyan & Zisserman, 2014) by 8× on CIFAR-100(Krizhevsky & Hinton, 2009) while maintaining accuracy on par with the teacher network. The automatically found compressed architectures can also achieve higher accuracy than the state-of-the-art manuallydesigned compact architecture ShuffleNet (Zhang et al., 2018) with a similar size. We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training.Contributions: (1) We propose a novel method to incrementally learn an embedding space over the domain of network architectures. Based on the learnable embedding space, we present a framework of searching for compressed network architectures with BO. The learned embedding provides a feature space over which the kernel function of BO is defined.(2)We propose a set of architecture operators for generating architectures for search. Operators for modifying the teacher network are: layer removal, layer shrinkage and skip connection addition.(3)We propose a multiple kernel strategy to prevent the premature convergence of the search and encourage the search algorithm to explore more diverse architectures during the search process. | LEARNABLE EMBEDDING SPACE FOR EFFICIENT NEURAL ARCHITECTURE COMPRESSION |
d253098810 | This paper investigates when one can efficiently recover an approximate Nash Equilibrium (NE) in offline congestion games. The existing dataset coverage assumption in offline general-sum games inevitably incurs a dependency on the number of actions, which can be exponentially large in congestion games. We consider three different types of feedback with decreasing revealed information. Starting from the facility-level (a.k.a., semi-bandit) feedback, we propose a novel one-unit deviation coverage condition and give a pessimism-type algorithm that can recover an approximate NE. For the agent-level (a.k.a., bandit) feedback setting, interestingly, we show the one-unit deviation coverage condition is not sufficient. On the other hand, we convert the game to multi-agent linear bandits and show that with a generalized data coverage assumption in offline linear bandits, we can efficiently recover the approximate NE. Lastly, we consider a novel type of feedback, the game-level feedback where only the total reward from all agents is revealed. Again, we show the coverage assumption for the agent-level feedback setting is insufficient in the game-level feedback setting, and with a stronger version of the data coverage assumption for linear bandits, we can recover an approximate NE. Together, our results constitute the first study of offline congestion games and imply formal separations between different types of feedback. * Equal contribution. . Nearly horizon-free offline reinforcement learning. Advances in neural information processing systems, 34, 2021. Robert W Rosenthal. A class of games possessing pure-strategy nash equilibria. International Journal of Game Theory, 2(1):65-67, 1973. Tim Roughgarden andÉva Tardos. Bounding the inefficiency of equilibria in nonatomic congestion games. Games and economic behavior, 47(2):389-403, 2004.William H Sandholm, Emin Dokumacı, and Ratul Lahkar. The projection dynamic and the replicator dynamic. Games and Economic Behavior, 64(2):666-683, 2008. . Solving discounted stochastic two-player games with near-optimal time and sample complexity. sample complexity of modelbased marl for general-sum markov games. arXiv preprint arXiv:2110.02355, 2021. | Offline congestion games: How feedback type affects data coverage requirement |
d263830421 | Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications.However, their demanding computation during inference has raised significant challenges for deployment on resource-constrained devices.Despite recent trends favoring alternative activation functions such as GELU or SiLU, known for increased computation, this study strongly advocates for reinstating ReLU activation in LLMs.We demonstrate that using the ReLU activation function has a negligible impact on convergence and performance while significantly reducing computation and weight transfer.This reduction is particularly valuable during the memory-bound inference step, where efficiency is paramount.Exploring sparsity patterns in ReLU-based LLMs, we unveil the reutilization of activated neurons for generating new tokens and leveraging these insights, we propose practical strategies to substantially reduce LLM inference computation up to three times, using ReLU activations with minimal performance trade-offs. | ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models |
d244488490 | Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. In this paper, we advocate that jointly solving these tasks leads to significant improvement for point cloud reconstruction. To this end, we propose a deep point cloud reconstruction network consisting of two stages: 1) a 3D sparse stacked-hourglass network as for the initial densification and denoising, 2) a refinement via transformers converting the discrete voxels into 3D points. In particular, we further improve the performance of transformer by a newly proposed module called amplified positional encoding. This module has been designed to differently amplify the magnitude of the positional encoding vectors based on the points' distances. Extensive experiments demonstrate that our network achieves state-of-the-art performance among the recent studies in the ScanNet, ICL-NUIM, and ShapeNetPart datasets. Moreover, we underline the ability of our network to generalize toward real-world and unmet scenes.Published as a conference paper at ICLR 2022 depicted inFig. 1. To the best of our knowledge, this paper is the first attempt to jointly resolve the inherent shortcomings of point cloud obtained from 3D scanning devices: sparsity, noise, irregularity, and outliers. To this end, we propose a deep point cloud reconstruction network that consists in two stages: a voxel generation network and a voxel re-localization network.In the first stage, the voxel generation network (Sec. 3.1) aims to densify voxels and remove outliers via sparse convolution layer(Choy et al., 2019). Rather than to use k-Nearest Neighbor that is sensitive to points' density (Mao et al., 2019), we utilize voxel hashing with sparse convolution layer to understand absolute-scale 3D structures for densification and denoising. Despite its success, voxelization inevitably leads to information loss due to the discretization process. To provide a fine-grained reconstruction, in the 2 nd stage, we propose a voxel re-localization network that converts discrete voxels into 3D points using transformer. Additionally, we increase the performance of transformer by the amplified positional encoding. In our analysis, spatial frequency plays an important role in the context of voxel-to-point conversion. Our new positional encoding strategy is useful to infer descriptive and detailed point cloud by simply changing the amplitude of the encoding vector. Our contributions can be summarized as follows:• New problem formulation: point cloud reconstruction.• Novel two-stage architecture for voxel-to-point refinement.• A large number of experiments illustrating the generalization ability of our point cloud reconstruction to real 3D scans.RELATED WORKSPoint cloud obtained from 3D scanning devices are known to contain various artifacts(Berger et al., 2017), such as noise, outliers, irregularity, and sparsity. Point cloud reconstruction aims at resolving these aforementioned issues in order to provide fine-grained 3D reconstructions. In this section, we introduce different approaches related to point cloud refinement. Point Cloud Denoising. Without extra information, such as RGB images, point cloud denoising purely based on the input point distribution is a challenging task (Lee, 2000; Avron et al., 2010). Recent deep learning-based strategies (Rakotosaona et al., 2020; Roveri et al., 2018; Hermosilla et al., 2019; Pistilli et al., 2020; Luo & Hu, 2021) demonstrate promising results. However, despite the large improvement provided by these architectures, these methods do not have the ability to densify the reconstruction. | DEEP POINT CLOUD RECONSTRUCTION |
d207847719 | Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs. Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it. Most of the existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representations of nodes only. In this paper, we propose COMPGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph. COMPGCN leverages a variety of entity-relation composition operations from Knowledge Graph Embedding techniques and scales with the number of relations. It also generalizes several of the existing multi-relational GCN methods. We evaluate our proposed method on multiple tasks such as node classification, link prediction, and graph classification, and achieve demonstrably superior results. We make the source code of COMPGCN available to foster reproducible research. | COMPOSITION-BASED MULTI-RELATIONAL GRAPH CONVOLUTIONAL NETWORKS |
d254070084 | A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD) leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training. However, to date, empirical evidence assessing the explanatory power of these hypotheses is lacking. In this paper, we conduct an extensive empirical evaluation, focusing on the ability of various theorized mechanisms to close the small-to-large batch generalization gap. Additionally, we characterize how the quantities that SGD has been claimed to (implicitly) regularize change over the course of training. By using micro-batches, i.e. disjoint smaller subsets of each mini-batch, we empirically show that explicitly penalizing the gradient norm or the Fisher Information Matrix trace, averaged over micro-batches, in the large-batch regime recovers small-batch SGD generalization, whereas Jacobian-based regularizations fail to do so. This generalization performance is shown to often be correlated with how well the regularized model's gradient norms resemble those of small-batch SGD. We additionally show that this behavior breaks down as the micro-batch size approaches the batch size. Finally, we note that in this line of inquiry, positive experimental findings on CIFAR10 are often reversed on other datasets like CIFAR100, highlighting the need to test hypotheses on a wider collection of datasets. * znovack@ucsd.com, UC San Diego. † skaur@princeton.edu, Princeton University. ‡ | Disentangling the Mechanisms Behind Implicit Regularization in SGD |
d249395201 | Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective in practice. In this paper, we propose Diffusion-GAN, a novel GAN framework that leverages a forward diffusion chain to generate Gaussianmixture distributed instance noise. Diffusion-GAN consists of three components, including an adaptive diffusion process, a diffusion timestep-dependent discriminator, and a generator. Both the observed and generated data are diffused by the same adaptive diffusion process. At each diffusion timestep, there is a different noise-to-data ratio and the timestep-dependent discriminator learns to distinguish the diffused real data from the diffused generated data. The generator learns from the discriminator's feedback by backpropagating through the forward diffusion chain, whose length is adaptively adjusted to balance the noise and data levels. We theoretically show that the discriminator's timestep-dependent strategy gives consistent and helpful guidance to the generator, enabling it to match the true data distribution. We demonstrate the advantages of Diffusion-GAN over strong GAN baselines on various datasets, showing that it can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs. | DIFFUSION-GAN: TRAINING GANS WITH DIFFUSION |
d234358843 | Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on "how to choose," the layerwise sparsities are mostly selected algorithm-byalgorithm, often resorting to handcrafted heuristics or an extensive hyperparameter search. To fill this gap, we propose a novel importance score for global pruning, coined layer-adaptive magnitude-based pruning (LAMP) score; the score is a rescaled version of weight magnitude that incorporates the model-level 2 distortion incurred by pruning, and does not require any hyperparameter tuning or heavy computation. Under various image classification setups, LAMP consistently outperforms popular existing schemes for layerwise sparsity selection. Furthermore, we observe that LAMP continues to outperform baselines even in weight-rewinding setups, while the connectivity-oriented layerwise sparsity (the strongest baseline overall) performs worse than a simple global magnitude-based pruning in this case. Code: https://github.com/jaeho-lee/layer-adaptive-sparsity Recent discoveries (Gale et al., 2019; Evci et al., 2020) demonstrate that, given an appropriate choice of layerwise sparsity, simply pruning on the basis of weight magnitude yields a surprisingly powerful unstructured pruning scheme. For instance, Gale et al. (2019) evaluates the performance of magnitudebased pruning (MP; Han et al. (2015); Zhu & Gupta(2018)) with an extensive hyperparameter tuning, and shows that MP achieves comparable or better performance than state-of-the-art pruning algorithms that use more complicated importance scores. To arrive at such a performance level, the authors introduce the following handcrafted heuristic: Leave the first convolutional layer fully dense, and prune up to only 80% of weights from the last fully-connected layer; the heuristic is motivated by the sparsity pattern from other state-of-the-art algorithms(Molchanov et al., 2017)and additional experimental/architectural observations. Unfortunately, there is an apparent lack of consensus on "how to choose the layerwise sparsity" for the magnitude-based pruning. Instead, the layerwise sparsity is selected mostly on an algorithm-byalgorithm basis. One common method is the global MP criteria (see, e.g., Morcos et al.(2019)), * Work done at KAIST 1 i.e., simultaneously training and pruning arXiv:2010.07611v2 [cs.LG] | LAYER-ADAPTIVE SPARSITY FOR THE MAGNITUDE-BASED PRUNING |
d20285896 | In this paper, we present a systematic study on GANs with categorical discriminator, especially their impact on the optimization scheme of the generator. We derive class-aware gradients and cross-entropy decomposition, to theoretically reveal how they help GAN training and the inherent problems in previous models. Based on the analysis, we propose an advanced model AM-GAN, along with an interesting dynamic labeling mechanism. We show mathematically that the proposed AM-GAN is a general one covering several major existing solutions that exploit categorical discriminator. Empirical experiments demonstrate the effectiveness of the proposed method, with state-of-the-art sample quality and fast convergence. | Activation Maximization Generative Adversarial Nets |
d221971208 | Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. We explore the use of expert representations for transfer with a simple, yet effective, strategy. We train a diverse set of experts by exploiting existing label structures, and use cheap-to-compute performance proxies to select the relevant expert for each target task. This strategy scales the process of transferring to new tasks, since it does not revisit the pre-training data during transfer. Accordingly, it requires little extra compute per target task, and results in a speed-up of 2-3 orders of magnitude compared to competing approaches. Further, we provide an adapter-based architecture able to compress many experts into a single model. We evaluate our approach on two different data sources and demonstrate that it outperforms baselines on over 20 diverse vision tasks in both cases. * Equal contribution. Order decided by a coin toss. | Scalable Transfer Learning with Expert Models |
d252693109 | A multitude of work has shown that machine learning-based medical diagnosis systems can be biased against certain subgroups of people. This has motivated a growing number of bias mitigation algorithms that aim to address fairness issues in machine learning. However, it is difficult to compare their effectiveness in medical imaging for two reasons. First, there is little consensus on the criteria to assess fairness. Second, existing bias mitigation algorithms are developed under different settings, e.g., datasets, model selection strategies, backbones, and fairness metrics, making a direct comparison and evaluation based on existing results impossible. In this work, we introduce MEDFAIR, a framework to benchmark the fairness of machine learning models for medical imaging. MEDFAIR covers eleven algorithms from various categories, ten datasets from different imaging modalities, and three model selection criteria. Through extensive experiments, we find that the under-studied issue of model selection criterion can have a significant impact on fairness outcomes; while in contrast, state-of-the-art bias mitigation algorithms do not significantly improve fairness outcomes over empirical risk minimization (ERM) in both in-distribution and out-of-distribution settings. We evaluate fairness from various perspectives and make recommendations for different medical application scenarios that require different ethical principles. Our framework provides a reproducible and easy-to-use entry point for the development and evaluation of future bias mitigation algorithms in deep learning. Code is available at https://github.com/ys-zong/MEDFAIR.Published as a conference paper at ICLR 2023 Figure 1: Components of MEDFAIR benchmark. therefore aim to help diagnosis algorithms learn predictive models that are robust to confounding factors related to sensitive attribute s (Mehrabi et al., 2021).Given the importance of ensuring fairness in medical applications and the special characteristics of medical data, we argue that a systematic and rigorous benchmark is needed to evaluate the bias mitigation algorithms for medical imaging. However, a straightforward comparison of algorithmic fairness for medical imaging is difficult, as there is no consensus on a single metric for fairness of medical imaging models. Group fairness (Dwork et al., 2012; Verma & Rubin, 2018) is a popular and intuitive definition adopted by many debiasing algorithms, which optimises for equal performance among subgroups. However, this can lead to a trade-off of increasing fairness by decreasing the performance of the advantaged group, reducing overall utility substantially. Doing so may violate the ethical principles of beneficence and non-maleficence (Beauchamp, 2003), especially for some medical applications where all subgroups need to be protected. There are also other fairness definitions, including individual fairness (Dwork et al., 2012), minimax fairness (Diana et al., 2021), counterfactual fairness (Kusner et al., 2017), etc. It is thus important to consider which definition should be used for evaluations.In addition to the use of differing evaluation metrics, different experimental designs used by existing studies prevent direct comparisons between algorithms based on the existing literature. Most obviously, each study tends to use different datasets to evaluate their debiasing algorithms, preventing direct comparisons of results. Furthermore, many bias mitigation studies focus on evaluating tabular data with low-capacity models (Madras et al., 2018; Zhao et al., 2019; Diana et al., 2021), and recent analysis has shown that their conclusions do not generalise to high-capacity deep networks used for the analysis of image data (Zietlow et al., 2022). A crucial but less obvious issue is the choice of model selection strategy for hyperparameter search and early stopping. Individual bias mitigation studies are divergent or vague in their model selection criteria, leading to inconsistent comparisons even if the same datasets are used. Finally, given the effort required to collect and annotate medical imaging data, models are usually deployed in a different domain than the domain used for data collection. (E.g., data collected at hospital A is used to train a model deployed at hospital B). While the maintenance of prediction quality across datasets has been well studied, it is unclear if fairness achieved within one dataset (in-distribution) holds under dataset shift (out-of-distribution).In order to address these challenges, we provide the first comprehensive fairness benchmark for medical imaging -MEDFAIR. We conduct extensive experiments across eleven algorithms, ten datasets, four sensitive attributes, and three model selection strategies to assess bias mitigation algorithms in both in-distribution and out-of-distribution settings. We report multiple evaluation metrics and conduct rigorous statistical tests to find whether any of the algorithms is significantly better. Having trained over 7,000 models using 6,800 GPU-hours, we have the following observations:• Bias widely exists in ERM models trained in different modalities, which is reflected in the predictive performance gap between different subgroups for multiple metrics. fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. | MEDFAIR: BENCHMARKING FAIRNESS FOR MEDICAL IMAGING |
d423406 | We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured models in which the trees are either provided as input or predicted using supervision from explicit treebank annotations, the tree structures in this work are optimized to improve performance on a downstream task. Experiments demonstrate the benefit of learning task-specific composition orders, outperforming both sequential encoders and recursive encoders based on treebank annotations. We analyze the induced trees and show that while they discover some linguistically intuitive structures (e.g., noun phrases, simple verb phrases), they are different than conventional English syntactic structures. | LEARNING TO COMPOSE WORDS INTO SENTENCES WITH REINFORCEMENT LEARNING |
d253553242 | Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strategies where the agents operate in a decentralized and symmetric manner with guaranteed convergence. While significant efforts have been made in understanding zero-sum two-player matrix games, the performance in zero-sum multiagent games remains inadequately explored, especially in the presence of delayed feedbacks, leaving the scalability and resiliency of gradient play open to questions. In this paper, we make progress by studying asynchronous gradient plays in zero-sum polymatrix games under delayed feedbacks. We first establish that the last iterate of entropy-regularized optimistic multiplicative weight updates (OMWU) method converges linearly to the quantal response equilibrium (QRE), the solution concept under bounded rationality, in the absence of delays. While the linear convergence continues to hold even when the feedbacks are randomly delayed under mild statistical assumptions, it converges at a noticeably slower rate due to a smaller tolerable range of learning rates. Moving beyond, we demonstrate entropy-regularized OMWUby adopting two-timescale learning rates in a delay-aware manner-enjoys faster last-iterate convergence under fixed delays, and continues to converge provably even when the delays are arbitrarily bounded in an average-iterate manner. Our methods also lead to finite-time guarantees to approximate the Nash equilibrium (NE) by moderating the amount of regularization. To the best of our knowledge, this work is the first that aims to understand asynchronous gradient play in zero-sum polymatrix games under a wide range of delay assumptions, highlighting the role of learning rates separation. | Asynchronous Gradient Play in Zero-Sum Multi-agent Games |
d5071138 | We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC contains 186,089 unique questionanswer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie -one from Wikipedia and the other from IMDb -written by two different authors. We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize answers from the other version. This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version. Further, since the two versions have different levels of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating external background knowledge. Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences. Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset(Rajpurkar et al., 2016b), even when coupled with tra-ditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset). This opens up several interesting research avenues wherein DuoRC could complement other RC datasets to explore novel neural approaches for studying language understanding. | DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension |
d264820183 | Recently video generation has achieved substantial progress with realistic results.Nevertheless, existing AI-generated videos are usually very short clips ("shotlevel") depicting a single scene.To deliver a coherent long video ("story-level"), it is desirable to have creative transition and prediction effects across different clips.This paper presents a short-to-long (S2L) video diffusion model, SEINE, that focuses on generative transition and prediction.The goal is to generate highquality long videos with smooth and creative transitions between scenes and varying lengths of shot-level videos.Specifically, we propose a random-mask video diffusion model to automatically generate transitions based on textual descriptions.By providing the images of different scenes as inputs, combined with textbased control, our model generates transition videos that ensure coherence and visual quality.Furthermore, the model can be readily extended to various tasks such as image-to-video animation and auto-regressive video prediction.To conduct a comprehensive evaluation of this new generative task, we propose three assessing criteria for smooth and creative transition: temporal consistency, semantic similarity, and video-text semantic alignment.Extensive experiments validate the effectiveness of our approach over existing methods for generative transition and prediction, enabling the creation of story-level long videos.Project page: https://vchitect.github.io/SEINE-project/. | SEINE: SHORT-TO-LONG VIDEO DIFFUSION MODEL FOR GENERATIVE TRANSITION AND PREDICTION |
d3698524 | We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demonstrate large empirical gains on a collection of simple but challenging distributions, for instance achieving a 106× improvement in effective sample size in one case, and mixing when standard HMC makes no measurable progress in a second. Finally, we show quantitative and qualitative gains on a real-world task: latent-variable generative modeling. We release an open source TensorFlow implementation of the algorithm. | GENERALIZING HAMILTONIAN MONTE CARLO WITH NEURAL NETWORKS |
d221836662 | We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere S d−1 . Additionally, we prove that the exponential power kernel with a smaller power (making the kernel more non-smooth) leads to a larger RKHS, when it is restricted to the sphere S d−1 and when it is defined on the entire R d .We make a final conclusion on this problem and show that the RKHS of the Laplace kernel and the NTK with any number of layers have the same set of functions, when they are both restricted to S d−1 . In other words, we prove the following theorem.Theorem 1. Let H Lap (S d−1 ) and H N k (S d−1 ) be the RKHS associated with the Laplace kernel K Lap (x, y) = e −c x−y (c > 0) and the neural tangent kernel of a (k + 1)-layer fully connected ReLU network. Both kernels are restricted to the sphere S d−1 . Then the two spaces include the same set of functions:Our second result is that the exponential power kernel with a smaller power (making the kernel more non-smooth) leads to a larger RKHS, both when it is restricted to the sphere S d−1 and when it is defined on the entire R d . | Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS |
d264306002 | We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances.While existing methods rely on interactive expert labeling, additional offline datasets, or domain-specific invariances, our approach requires minimal additional assumptions beyond access to expert data.The key insight is to leverage local continuity in the environment dynamics to generate corrective labels.Our method first constructs a dynamics model from the expert demonstration, encouraging local Lipschitz continuity in the learned model.In locally continuous regions, this model allows us to generate corrective labels within the neighborhood of the demonstrations but beyond the actual set of states and actions in the dataset.Training on this augmented data enhances the agent's ability to recover from perturbations and deal with compounding errors.We demonstrate the effectiveness of our generated labels through experiments in a variety of robotics domains in simulation that have distinct forms of continuity and discontinuity, including classic control problems, drone flying, navigation with high-dimensional sensor observations, legged locomotion, and tabletop manipulation. | CCIL: CONTINUITY-BASED DATA AUGMENTATION FOR CORRECTIVE IMITATION LEARNING |
d211027382 | As gradual typing becomes increasingly popular in languages like Python and TypeScript, there is a growing need to infer type annotations automatically. While type annotations help with tasks like code completion and static error catching, these annotations cannot be fully determined by compilers and are tedious to annotate by hand. This paper proposes a probabilistic type inference scheme for TypeScript based on a graph neural network. Our approach first uses lightweight source code analysis to generate a program abstraction called a type dependency graph, which links type variables with logical constraints as well as name and usage information. Given this program abstraction, we then use a graph neural network to propagate information between related type variables and eventually make type predictions. Our neural architecture can predict both standard types, like number or string, as well as user-defined types that have not been encountered during training. Our experimental results show that our approach outperforms prior work in this space by 14% (absolute) on library types, while having the ability to make type predictions that are out of scope for existing techniques. | LAMBDANET: PROBABILISTIC TYPE INFERENCE USING GRAPH NEURAL NETWORKS |
d3482308 | In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU). We give the first-ever polynomial time (in the size of data) algorithm to train to global optimality a ReLU DNN with one hidden layer, assuming the input dimension and number of nodes of the network as fixed constants.We also improve on the known lower bounds on size (from exponential to super exponential) for approximating a ReLU deep net function by a shallower ReLU net. Our gap theorems hold for smoothly parametrized families of "hard" functions, contrary to countable, discrete families known in the literature. An example consequence of our gap theorems is the following: for every natural number k there exists a function representable by a ReLU DNN with k 2 hidden layers and total size k 3 , such that any ReLU DNN with at most k hidden layers will require at least 1 2 k k+1 − 1 total nodes.Finally, we construct a family of R n → R piecewise linear functions for n ≥ 2 (also smoothly parameterized), whose number of affine pieces scales exponentially with the dimension n at any fixed size and depth. To the best of our knowledge, such a construction with exponential dependence on n has not been achieved by previous families of "hard" functions in the neural nets literature. This construction utilizes the theory of zonotopes from polyhedral theory. | Understanding Deep Neural Networks with Rectified Linear Units |
d7070838 | In implicit models, one often interpolates between sampled points in latent space. As we show in this paper, care needs to be taken to match-up the distributional assumptions on code vectors with the geometry of the interpolating paths. Otherwise, typical assumptions about the quality and semantics of in-between points may not be justified. Based on our analysis we propose to modify the prior code distribution to put significantly more probability mass closer to the origin. As a result, linear interpolation paths are not only shortest paths, but they are also guaranteed to pass through high-density regions, irrespective of the dimensionality of the latent space. Experiments on standard benchmark image datasets demonstrate clear visual improvements in the quality of the generated samples and exhibit more meaningful interpolation paths. | SEMANTIC INTERPOLATION IN IMPLICIT MODELS |
d262054014 | Fine-tuning (via methods such as instruction-tuning or reinforcement learning from human feedback) is a crucial step in training language models to robustly carry out tasks of interest.However, we lack a systematic understanding of the effects of fine-tuning, particularly on tasks outside the narrow fine-tuning distribution.In a simplified scenario, we demonstrate that improving performance on tasks within the fine-tuning data distribution comes at the expense of suppressing model capabilities on other tasks.This degradation is especially pronounced for tasks "closest" to the fine-tuning distribution.We hypothesize that language models implicitly infer the task of the prompt corresponds, and the fine-tuning process predominantly skews this task inference towards tasks in the fine-tuning distribution.To test this hypothesis, we propose Conjugate Prompting to see if we can recover pretrained capabilities.Conjugate prompting artificially makes the task look farther from the fine-tuning distribution while requiring the same capability.We find that conjugate prompting systematically recovers some of the pretraining capabilities on our synthetic setup.We then apply conjugate prompting to real-world LLMs using the observation that fine-tuning distributions are typically heavily skewed towards English.We find that simply translating the prompts to different languages can cause the fine-tuned models to respond like their pretrained counterparts instead.This allows us to recover the in-context learning abilities lost via instruction tuning, and more concerningly, to recover harmful content generation suppressed by safety fine-tuning in chatbots like ChatGPT. | UNDERSTANDING CATASTROPHIC FORGETTING IN LANGUAGE MODELS VIA IMPLICIT INFERENCE |
d251648935 | 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. | EXPLANATIONS OF BLACK-BOX MODELS BASED ON DIRECTIONAL FEATURE INTERACTIONS |
d238744324 | We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expert data in a contextual MDP setup. We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms to fit this setup. We then discuss the problem of distribution shift between the expert data and the online environment when the data is only partially observable. We prove possibility and impossibility results for imitation learning under arbitrary distribution shift of the missing covariates. When additional external reward is provided, we propose a sampling procedure that addresses the unknown shift and prove convergence to an optimal solution. Finally, we validate our claims empirically on challenging assistive healthcare and recommender system simulation tasks. | ON COVARIATE SHIFT OF LATENT CONFOUNDERS IN IMITATION AND REINFORCEMENT LEARNING |
d253080775 | As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (QA) benchmarks that attempt to assess reasoning are often limited by a narrow scope of covered situations and subject matters. We introduce WIKIWHY, a QA dataset built around a novel auxiliary task: explaining why an answer is true in natural language. WIKIWHY contains over 9,000 "why" question-answer-rationale triples, grounded on Wikipedia facts across a diverse set of topics. Each rationale is a set of supporting statements connecting the question to the answer. | WIKIWHY: ANSWERING AND EXPLAINING CAUSE-AND-EFFECT QUESTIONS |
d67856222 | Batch Normalization (BN) is a common technique used to speed-up and stabilize training. On the other hand, the learnable parameters of BN are commonly used in conditional Generative Adversarial Networks (cGANs) for representing classspecific information using conditional Batch Normalization (cBN). In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization. We show that our conditional Coloring can represent categorical conditioning information which largely helps the cGAN qualitative results. Moreover, we show that full-feature whitening is important in a general GAN scenario in which the training process is known to be highly unstable. We test our approach on different datasets and using different GAN networks and training protocols, showing a consistent improvement in all the tested frameworks. Our CIFAR-10 conditioned results are higher than all previous works on this dataset. | WHITENING AND COLORING BATCH TRANSFORM FOR GANS |
d246863450 | Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, growing a policy population that is robust to exploit. This iterative approach suffers from two issues in real-world games: a) under finite budget, approximate best-response operators at each iteration needs truncating, resulting in under-trained good-responses populating the population; b) repeated learning of basic skills at each iteration is wasteful and becomes intractable in the presence of increasingly strong opponents. In this work, we propose Neural Population Learning (NeuPL) as a solution to both issues. NeuPL offers convergence guarantees to a population of best-responses under mild assumptions. By representing a population of policies within a single conditional model, NeuPL enables transfer learning across policies. Empirically, we show the generality, improved performance and efficiency of NeuPL across several test domains 1 . Most interestingly, we show that novel strategies become more accessible, not less, as the neural population expands.The need for learning not one, but a population of strategies is rooted in classical game theory. Consider the purely cyclical game of rock-paper-scissors, the performance of individual strategies is meaningless as improving against one entails losing to another. By contrast, performance can be meaningfully examined between populations. A population consisting of pure strategies {rock, paper} does well against a singleton population of {scissors} because in the meta-game where both populations are revealed, a player picking strategies from the former can always beat a player choosing from the latter 2 . This observation underpins the unifying population learning framework of Policy Space Response Oracle (PSRO) where a new policy is trained to best-respond to a mixture over previous policies at each iteration, following a meta-strategy solver(Lanctot et al., 2017). Most impressively, Vinyals et al. (2019) explored the strategy game of StarCraft with a league of policies, using a practical variation of PSRO. The league counted close to a thousand sophisticated deep RL agents as the population collectively became robust to exploits.Unfortunately, such empirical successes often come at considerable costs. Population learning algorithms with theoretical guarantees are traditionally studied in normal-form games(Brown, 1951;McMahan et al., 2003)where best-responses can be solved exactly. This is in stark contrast to real-world Game-of-Skills (Czarnecki et al., 2020) -such games are often temporal in nature, where best-responses can only be approximated with computationally intensive methods (e.g. deep RL). This has two implications. First, for a given opponent, one cannot efficiently tell apart good-responses that temporarily plateaued at local optima from globally optimal best-responses. As a result, approximate best-response operators are often truncated prematurely, according to hand-crafted schedules(Lanctot et al., 2017;Mcaleer et al., 2020). Second, real-world games often afford strategy-agnostic transitive | NEUPL: NEURAL POPULATION LEARNING |
d247593929 | Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, participants have heterogeneous resources due to disparities in hardware and inference dynamics that require quickly loading models of different sizes and levels of robustness. The heterogeneity and dynamics together impose significant challenges to existing FL approaches and thus greatly limit FL's applicability. In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness. Specifically, we achieve customization by learning a set of base sub-networks of different sizes and robustness levels, which are later aggregated on-demand according to inference requirements. This split-mix strategy achieves customization with high efficiency in communication, storage, and inference. Extensive experiments demonstrate that our method provides better in-situ customization than the existing heterogeneous-architecture FL methods. Codes and pre-trained models are available: https://github.com/illidanlab/SplitMix. Published as a conference paper at ICLR 2022 (a) Illustration of FedAvg (McMahan et al., 2017) with a device-incompatible model and a heterogenousarchitecture variant (HeteroFL) (Diao et al., 2021) with under-trained wide models.(b) The proposed Split-Mix framework provides in-situ customization of widths and adversarial robustness to address heterogeneity and dynamics, enabling efficient training and inference. In this example, we use a subnet with 1/4 channels (or widths) per layer as a base model for model-width customization. For simplicity, we denote it ×0.25 net, and a ×1 net can be split into 4 ×0.25 base models. | EFFICIENT SPLIT-MIX FEDERATED LEARNING FOR ON-DEMAND AND IN-SITU CUSTOMIZATION |
d252918639 | Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce inference time and memory requirements. However, quantifying and predicting the response of a model to the changes associated with this procedure remains challenging. This response is non-linear and heterogeneous throughout the network. Understanding which groups of parameters and activations are more sensitive to quantization than others is a critical stage in maximizing efficiency. For this purpose, we propose FIT. Motivated by an information geometric perspective, FIT combines the Fisher information with a model of quantization. We find that FIT can estimate the final performance of a network without retraining. FIT effectively fuses contributions from both parameter and activation quantization into a single metric. Additionally, FIT is fast to compute when compared to existing methods, demonstrating favourable convergence properties. These properties are validated experimentally across hundreds of quantization configurations, with a focus on layer-wise mixed-precision quantization. | FIT: A METRIC FOR MODEL SENSITIVITY |
d219708206 | The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn PDEs directly from data, previous methods are limited to discretetime approximations or make the limiting assumption of the observations arriving at regular grids. We propose a general continuous-time differential model for dynamical systems whose governing equations are parameterized by message passing graph neural networks. The model admits arbitrary space and time discretizations, which removes constraints on the locations of observation points and time intervals between the observations. The model is trained with continuous-time adjoint method enabling efficient neural PDE inference. We demonstrate the model's ability to work with unstructured grids, arbitrary time steps, and noisy observations. We compare our method with existing approaches on several well-known physical systems that involve first and higher-order PDEs with state-of-the-art predictive performance.Preprint. Under review. | Learning continuous-time PDEs from sparse data with graph neural networks |
d239050236 | Schrödinger Bridge (SB) is an entropy-regularized optimal transport problem that has received increasing attention in deep generative modeling for its mathematical flexibility compared to the Scored-based Generative Model (SGM). However, it remains unclear whether the optimization principle of SB relates to the modern training of deep generative models, which often rely on constructing log-likelihood objectives.This raises questions on the suitability of SB models as a principled alternative for generative applications. In this work, we present a novel computational framework for likelihood training of SB models grounded on Forward-Backward Stochastic Differential Equations Theory -a mathematical methodology appeared in stochastic optimal control that transforms the optimality condition of SB into a set of SDEs. Crucially, these SDEs can be used to construct the likelihood objectives for SB that, surprisingly, generalizes the ones for SGM as special cases. This leads to a new optimization principle that inherits the same SB optimality yet without losing applications of modern generative training techniques, and we show that the resulting training algorithm achieves comparable results on generating realistic images on MNIST, CelebA, and CIFAR10. Our code is available at https://github.com/ghliu/SB-FBSDE. * Equal contribution. Order determined by coin flip. See Author Contributions section.arXiv:2110.11291v5 [stat.ML] 3 Apr 2023Published as a conference paper at ICLR 2022 two problems (i.e. both involve transforming distributions) is evident, and the additional flexibility from SB is also attractive. To enable SB-inspired generative training, however, previous works require either ad-hoc multi-stage optimization or retreat to traditional SB algorithms, e.g. Iterative Proportional Fitting (IPF;Kullback (1968)). The underlying relation between the optimization principle of SB and modern generative training, in particular SGM, remains relatively unexplored, despite their intimately related problem formulations. More importantly, with the recent connection between SGM and log-likelihood computation(Song et al., 2021), it is crucial to explore whether there exists an alternative way of training SB that better respects, or perhaps generalizes, modern training of SGM, so as to solidify the suitability of SB as a principled generative model. | LIKELIHOOD TRAINING OF SCHRÖDINGER BRIDGE USING FORWARD-BACKWARD SDES THEORY |
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