mtybilly/Instruct2DS huggingface dataset:
Adversarially Robust Change Point Detection
Authors: Mengchu Li, Yi Yu
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Change point detection is becoming increasingly popular in many application areas. On one hand, most of the theoretically-justified methods are investigated in an ideal setting without model violations, or merely robust against identical heavy-tailed noise distribution across time and/or against isolate outliers; on the other hand, we are aware that there have been exponentially growing attacks from adversaries, who may pose systematic contamination on data to purposely create spurious change points or disguise true change points. In light of the timely need for a change point detection method that is robust against adversaries, we start with, arguably, the simplest univariate mean change point detection problem. The adversarial attacks are formulated through the Huber $\varepsilon$-contamination framework, which in particular allows the contamination distributions to be different at each time point. In this paper, we demonstrate a phase transition phenomenon in change point detection. This detection boundary is a function of the contamination proportion~$\varepsilon$ and is the first time shown in the literature. In addition, we derive the minimax-rate optimal localisation error rate, quantifying the cost of accuracy in terms of the contamination proportion. We propose a computationally feasible method, matching the minimax lower bound under certain conditions, saving for logarithmic factors. Extensive numerical experiments are conducted with comparisons to robust change point detection methods in the existing literature.
Cycle Self-Training for Domain Adaptation
Authors: Hong Liu, Jianmin Wang, Mingsheng Long
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to narrow the domain shift, which are empirically effective but theoretically challenged by the hardness or impossibility theorems. Recently, self-training has been gaining momentum in UDA, which exploits unlabeled target data by training with target pseudo-labels. However, as corroborated in this work, under distributional shift, the pseudo-labels can be unreliable in terms of their large discrepancy from target ground truth. In this paper, we propose Cycle Self-Training (CST), a principled self-training algorithm that explicitly enforces pseudo-labels to generalize across domains. CST cycles between a forward step and a reverse step until convergence. In the forward step, CST generates target pseudo-labels with a source-trained classifier. In the reverse step, CST trains a target classifier using target pseudo-labels, and then updates the shared representations to make the target classifier perform well on the source data. We introduce the Tsallis entropy as a confidence-friendly regularization to improve the quality of target pseudo-labels. We analyze CST theoretically under realistic assumptions, and provide hard cases where CST recovers target ground truth, while both invariant feature learning and vanilla self-training fail. Empirical results indicate that CST significantly improves over the state-of-the-arts on visual recognition and sentiment analysis benchmarks.
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation
Authors: Bingchen Zhao, Kai Han
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: In this paper, we tackle the problem of novel visual category discovery, i.e., grouping unlabelled images from new classes into different semantic partitions by leveraging a labelled dataset that contains images from other different but relevant categories.  This is a more realistic and challenging setting than conventional semi-supervised learning. We propose a two-branch learning framework for this problem, with one branch focusing on local part-level information and the other branch focusing on overall characteristics. To transfer knowledge from the labelled data to the unlabelled, we propose using dual ranking statistics on both branches to generate pseudo labels for training on the unlabelled data. We further introduce a mutual knowledge distillation method to allow information exchange and encourage agreement between the two branches for discovering new categories, allowing our model to enjoy the benefits of global and local features. We comprehensively evaluate our method on public benchmarks for generic object classification, as well as the more challenging datasets for fine-grained visual recognition, achieving state-of-the-art performance.
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Authors: Fuchao Wei, Chenglong Bao, Yang Liu
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Anderson mixing (AM) is an acceleration method for fixed-point iterations. Despite its success and wide usage in scientific computing, the convergence theory of AM remains unclear, and its applications to machine learning problems are not well explored. In this paper, by introducing damped projection and adaptive regularization to the classical AM, we propose a Stochastic Anderson Mixing (SAM) scheme to solve nonconvex stochastic optimization problems. Under mild assumptions, we establish the convergence theory of SAM, including the almost sure convergence to stationary points and the worst-case iteration complexity. Moreover, the complexity bound can be improved when randomly choosing an iterate as the output. To further accelerate the convergence, we incorporate a variance reduction technique into the proposed SAM. We also propose a preconditioned mixing strategy for SAM which can empirically achieve faster convergence or better generalization ability. Finally, we apply the SAM method to train various neural networks including the vanilla CNN, ResNets, WideResNet, ResNeXt, DenseNet and LSTM. Experimental results on image classification and language model demonstrate the advantages of our method.
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model
Authors: Bingyan Wang, Yuling Yan, Jianqing Fan
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The curse of dimensionality is a widely known issue in reinforcement learning (RL). In the tabular setting where the state space $\mathcal{S}$ and the action space $\mathcal{A}$ are both finite, to obtain a near optimal policy with sampling access to a generative model, the minimax optimal sample complexity scales linearly with $|\mathcal{S}|\times|\mathcal{A}|$, which can be prohibitively large when $\mathcal{S}$ or $\mathcal{A}$ is large. This paper considers a Markov decision process (MDP) that admits a set of state-action features, which can linearly express (or approximate) its probability transition kernel. We show that a model-based approach (resp.$~$Q-learning) provably learns an $\varepsilon$-optimal policy (resp.$~$Q-function) with high probability as soon as the sample size exceeds the order of $\frac{K}{(1-\gamma)^{3}\varepsilon^{2}}$ (resp.$~$$\frac{K}{(1-\gamma)^{4}\varepsilon^{2}}$), up to some logarithmic factor. Here $K$ is the feature dimension and $\gamma\in(0,1)$ is the discount factor of the MDP. Both sample complexity bounds are provably tight, and our result for the model-based approach matches the minimax lower bound. Our results show that for arbitrarily large-scale MDP, both the model-based approach and Q-learning are sample-efficient when $K$ is relatively small, and hence the title of this paper.
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs
Authors: Evan McCarty, Qi Zhao, Anastasios Sidiropoulos, Yusu Wang
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Recent years have witnessed a surge of approaches to use neural networks to help tackle combinatorial optimization problems, including graph optimization problems. However, theoretical understanding of such approaches remains limited. In this paper, we consider the geometric setting, where graphs are induced by points in a fixed dimensional Euclidean space. We show that several graph optimization problems can be approximated by an algorithm that is polynomial in graph size n via a framework we propose, call the Baker-paradigm. More importantly, a key advantage of the Baker-paradigm is that it decomposes the input problem into (at most linear number of) small sub-problems of fixed sizes (independent of the size of the input). For the family of such fixed-size sub-problems, we can now design neural networks with universal approximation guarantees to solve them. This leads to a mixed algorithmic-ML framework, which we call NN-Baker that has the capacity to approximately solve a family of graph optimization problems (e.g, maximum independent set and minimum vertex cover) in time linear to input graph size, and only polynomial to approximation parameter. We instantiate our NN-Baker by a CNN version and GNN version, and demonstrate the effectiveness and efficiency of our approach via a range of experiments.
A Note on Sparse Generalized Eigenvalue Problem
Authors: Yunfeng Cai, Guanhua Fang, Ping Li
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The sparse generalized eigenvalue problem (SGEP) aims to find the leading eigenvector with sparsity structure. SGEP plays an important role in statistical learning and has wide applications including, but not limited to, sparse principal component analysis, sparse canonical correlation analysis  and sparse Fisher discriminant analysis, etc. Due to the sparsity constraint, the solution of SGEP entails interesting properties from both numerical and statistical perspectives. In this paper, we provide a detailed sensitivity analysis for SGEP and establish the rate-optimal perturbation bound under the sparse setting. Specifically, we show that the bound is related to the perturbation/noise level and the recovery of the true support of the leading eigenvector as well. We also investigate the estimator of SGEP via imposing a non-convex regularization. Such estimator can achieve the optimal error rate and can recover the sparsity structure as well. Extensive numerical experiments corroborate our theoretical findings via using alternating direction method of multipliers (ADMM)-based computational method.
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Authors: Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR for decentralized execution. Then, to handle the temporal nature of the stochastic outcomes during executions, we propose a dynamic risk level predictor for risk level tuning. Finally, we optimize the CVaR policies with CVaR values used to estimate the target in TD error during centralized training and the CVaR values are used as auxiliary local rewards to update the local distribution via Quantile Regression loss. Empirically, we show that our method outperforms many state-of-the-art methods on various multi-agent risk-sensitive navigation scenarios and challenging StarCraft II cooperative tasks, demonstrating enhanced coordination and revealing improved sample efficiency.
Optimal Policies Tend To Seek Power
Authors: Alex Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Some researchers speculate that intelligent reinforcement learning (RL) agents would be incentivized to seek resources and power in pursuit of the objectives we specify for them. Other researchers point out that RL agents need not have human-like power-seeking instincts. To clarify this discussion, we develop the first formal theory of the statistical tendencies of optimal policies. In the context of Markov decision processes, we prove that certain environmental symmetries are sufficient for optimal policies to tend to seek power over the environment. These symmetries exist in many environments in which the agent can be shut down or destroyed. We prove that in these environments, most reward functions make it optimal to seek power by keeping a range of options available and, when maximizing average reward, by navigating towards larger sets of potential terminal states.
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Authors: Łukasz Kuciński, Tomasz Korbak, Paweł Kołodziej, Piotr Miłoś
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Communication is compositional if complex signals can be represented as a combination of simpler subparts. In this paper, we theoretically show that inductive biases on both the training framework and the data are needed to develop a compositional communication.  Moreover, we prove that compositionality spontaneously arises in the signaling games, where agents communicate over a noisy channel.  We experimentally confirm that a range of noise levels, which depends on the model and the data, indeed promotes compositionality. Finally, we provide a comprehensive study of this dependence and report results in terms of recently studied compositionality metrics: topographical similarity, conflict count, and context independence.
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
Authors: Zimin Chen, Vincent J Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Machine learning for understanding and editing source code has recently attracted significant interest, with many developments in new models, new code representations, and new tasks.This proliferation can appear disparate and disconnected, making each approach seemingly unique and incompatible, thus obscuring the core machine learning challenges and contributions.In this work, we demonstrate that the landscape can be significantly simplified by taking a general approach of mapping a graph to a sequence of tokens and pointers.Our main result is to show that 16 recently published tasks of different shapes can be cast in this form, based on which a single model architecture achieves near or above state-of-the-art results on nearly all tasks, outperforming custom models like code2seq and alternative generic models like Transformers.This unification further enables multi-task learning and a series of cross-cutting experiments about the importance of different modeling choices for code understanding and repair tasks.The full framework, called PLUR, is easily extensible to more tasks, and will be open-sourced (https://github.com/google-research/plur).
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
Authors: Yu Meng, Chenyan Xiong, Payal Bajaj, saurabh tiwary, Paul Bennett, Jiawei Han, XIA SONG
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: We present a self-supervised learning framework, COCO-LM, that pretrains Language Models by COrrecting and COntrasting corrupted text sequences. Following ELECTRA-style pretraining, COCO-LM employs an auxiliary language model to corrupt text sequences, upon which it constructs two new tasks for pretraining the main model. The first token-level task, Corrective Language Modeling, is to detect and correct tokens replaced by the auxiliary model, in order to better capture token-level semantics. The second sequence-level task, Sequence Contrastive Learning, is to align text sequences originated from the same source input while ensuring uniformity in the representation space. Experiments on GLUE and SQuAD demonstrate that COCO-LM not only outperforms recent state-of-the-art pretrained models in accuracy, but also improves pretraining efficiency. It achieves the MNLI accuracy of ELECTRA with 50% of its pretraining GPU hours. With the same pretraining steps of standard base/large-sized models, COCO-LM outperforms the previous best models by 1+ GLUE average points.
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization
Authors: Qi Deng, Wenzhi Gao
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Stochastic model-based methods have received increasing attention lately due to their appealing robustness to the stepsize selection and provable efficiency guarantee. We make two important extensions for improving model-based methods on stochastic weakly convex optimization. First, we propose new minibatch model- based methods by involving a set of samples to approximate the model function in each iteration. For the first time, we show that stochastic algorithms achieve linear speedup over the batch size even for non-smooth and non-convex (particularly, weakly convex) problems. To this end, we develop a novel sensitivity analysis of the proximal mapping involved in each algorithm iteration. Our analysis appears to be of independent interests in more general settings. Second, motivated by the success of momentum stochastic gradient descent, we propose a new stochastic extrapolated model-based method, greatly extending the classic Polyak momentum technique to a wider class of stochastic algorithms for weakly convex optimization. The rate of convergence to some natural stationarity condition is established over a fairly flexible range of extrapolation terms.While mainly focusing on weakly convex optimization, we also extend our work to convex optimization. We apply the minibatch and extrapolated model-based methods to stochastic convex optimization, for which we provide a new complexity bound and promising linear speedup in batch size. Moreover, an accelerated model-based method based on Nesterov’s momentum is presented, for which we establish an optimal complexity bound for reaching optimality.
XDO: A Double Oracle Algorithm for Extensive-Form Games
Authors: Stephen McAleer, JB Lanier, Kevin A Wang, Pierre Baldi, Roy Fox
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Policy Space Response Oracles (PSRO) is a reinforcement learning (RL) algorithm for two-player zero-sum games that has been empirically shown to find approximate Nash equilibria in large games. Although PSRO is guaranteed to converge to an approximate Nash equilibrium and can handle continuous actions, it may take an exponential number of iterations as the number of information states (infostates) grows. We propose Extensive-Form Double Oracle (XDO), an extensive-form double oracle algorithm for two-player zero-sum games that is guaranteed to converge to an approximate Nash equilibrium linearly in the number of infostates. Unlike PSRO, which mixes best responses at the root of the game, XDO mixes best responses at every infostate. We also introduce Neural XDO (NXDO), where the best response is learned through deep RL. In tabular experiments on Leduc poker, we find that XDO achieves an approximate Nash equilibrium in a number of iterations an order of magnitude smaller than PSRO. Experiments on a modified Leduc poker game and Oshi-Zumo show that tabular XDO achieves a lower exploitability than CFR with the same amount of computation. We also find that NXDO outperforms PSRO and NFSP on a sequential multidimensional continuous-action game. NXDO is the first deep RL method that can find an approximate Nash equilibrium in high-dimensional continuous-action sequential games.
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
Authors: Vihari Piratla, Soumen Chakrabarti, Sunita Sarawagi
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Our goal is to evaluate the accuracy of a black-box classification model, not as a single aggregate on a given test data distribution, but as a surface over a large number of combinations of attributes characterizing multiple test data distributions.  Such attributed accuracy measures become important as machine learning models get deployed as a service, where the training data distribution is hidden from clients, and different clients may be interested in diverse regions of the data distribution. We present Attributed Accuracy Assay (AAA) --- a Gaussian Process (GP)-based probabilistic estimator for such an accuracy surface. Each attribute combination, called an 'arm' is associated with a Beta density from which the service's accuracy is sampled.  We expect the GP to smooth the parameters of the Beta density over related arms to mitigate sparsity. We show that obvious application of GPs cannot address the challenge of heteroscedastic uncertainty over a huge attribute space that is sparsely and unevenly populated. In response, we present two enhancements: pooling sparse observations, and regularizing the scale parameter of the Beta densities. After introducing these innovations, we establish the effectiveness of AAA both in terms of its estimation accuracy and exploration efficiency, through extensive experiments and analysis.
A mechanistic multi-area recurrent network model of decision-making
Authors: Michael Kleinman, Chandramouli Chandrasekaran, Jonathan Kao
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Recurrent neural networks (RNNs) trained on neuroscience-based tasks have been widely used as models for cortical areas performing analogous tasks. However, very few tasks involve a single cortical area, and instead require the coordination of multiple brain areas. Despite the importance of multi-area computation, there is a limited understanding of the principles underlying such computation. We propose to use multi-area RNNs with neuroscience-inspired architecture constraints to derive key features of multi-area computation. In particular, we show that incorporating multiple areas and Dale's Law is critical for biasing the networks to learn biologically plausible solutions. Additionally, we leverage the full observability of the RNNs to show that output-relevant information is preferentially propagated between areas. These results suggest that cortex uses modular computation to generate minimal sufficient representations of task information. More broadly, our results suggest that constrained multi-area RNNs can produce experimentally testable hypotheses for computations that occur within and across multiple brain areas, enabling new insights into distributed computation in neural systems.
Learning to Compose Visual Relations
Authors: Nan Liu, Shuang Li, Yilun Du, Josh Tenenbaum, Antonio Torralba
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The visual world around us can be described as a structured set of objects and their associated relations. An image of a room may be conjured given only the description of the underlying objects and their associated relations. While there has been significant work on designing deep neural networks which may compose individual objects together, less work has been done on composing the individual relations between objects. A principal difficulty is that while the placement of objects is mutually independent, their relations are entangled and dependent on each other. To circumvent this issue, existing works primarily compose relations by utilizing a holistic encoder, in the form of text or graphs. In this work, we instead propose to represent each relation as an unnormalized density (an energy-based model), enabling us to compose separate relations in a factorized manner. We show that such a factorized decomposition allows the model to both generate and edit scenes that have multiple sets of relations more faithfully. We further show that decomposition enables our model to effectively understand the underlying relational scene structure.
Identity testing for Mallows model
Authors: Róbert Busa-Fekete, Dimitris Fotakis, Balazs Szorenyi, Emmanouil Zampetakis
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: In this paper, we devise identity tests for ranking data that is generated from Mallows model both in the \emph{asymptotic} and \emph{non-asymptotic} settings. First we consider the case when the central ranking is known, and devise two algorithms for testing the spread parameter of the Mallows model. The first one is obtained by constructing a Uniformly Most Powerful Unbiased (UMPU) test in the asymptotic setting and then converting it into a sample-optimal non-asymptotic identity test. The resulting test is, however, impractical even for medium sized data, because it requires computing the distribution of the sufficient statistic. The second non-asymptotic test is derived from an optimal learning algorithm for the Mallows model. This test is both easy to compute and is sample-optimal for a wide range of parameters. Next, we consider testing Mallows models for the unknown central ranking case. This case can be tackled in the asymptotic setting by introducing a bias that exponentially decays with the sample size. We support all our findings with extensive numerical experiments and show that the proposed tests scale gracefully with the number of items to be ranked.
Bandits with Knapsacks beyond the Worst Case
Authors: Karthik Abinav Sankararaman, Aleksandrs Slivkins
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Bandits with Knapsacks (BwK) is a general model for multi-armed bandits under supply/budget constraints. While worst-case regret bounds for BwK are well-understood, we present three results that go beyond the worst-case perspective. First, we provide upper and lower bounds which amount to a full characterization for logarithmic, instance-dependent regret rates.Second, we consider "simple regret" in BwK, which tracks algorithm's performance in a given round, and prove that it is small in all but a few rounds. Third, we provide a "generalreduction" from BwK to bandits which takes advantage of some known helpful structure, and apply this reduction to combinatorial semi-bandits, linear contextual bandits, and multinomial-logit bandits. Our results build on the BwK algorithm from prior work, providing new analyses thereof.
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Authors: Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, alexander gimson, Mihaela van der Schaar
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Significant effort has been placed on developing decision support tools to improve patient care. However, drivers of real-world clinical decisions in complex medical scenarios are not yet well-understood, resulting in substantial gaps between these tools and practical applications. In light of this, we highlight that more attention on understanding clinical decision-making is required both to elucidate current clinical practices and to enable effective human-machine interactions. This is imperative in high-stakes scenarios with scarce available resources. Using organ transplantation as a case study, we formalize the desiderata of methods for understanding clinical decision-making. We show that most existing machine learning methods are insufficient to meet these requirements and propose iTransplant, a novel data-driven framework to learn the factors affecting decisions on organ offers in an instance-wise fashion directly from clinical data, as a possible solution. Through experiments on real-world liver transplantation data from OPTN, we demonstrate the use of iTransplant to: (1) discover which criteria are most important to clinicians for organ offer acceptance; (2) identify patient-specific organ preferences of clinicians allowing automatic patient stratification;  and (3) explore variations in transplantation practices between different transplant centers. Finally, we emphasize that the insights gained by iTransplant can be used to inform the development of future decision support tools.
Change Point Detection via Multivariate Singular Spectrum Analysis
Authors: Arwa Alanqary, Abdullah Alomar, Devavrat Shah
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The objective of change point detection (CPD) is to detect significant and abrupt changes in the dynamics of the underlying system of interest through multivariate time series observations. In this work, we develop and analyze an algorithm for CPD that is inspired by a variant of the classical singular spectrum analysis (SSA) approach for time series by combining it with the classical cumulative sum (CUSUM) statistic from sequential hypothesis testing. In particular, we model the underlying dynamics of multivariate time series observations through the spatio-temporal model introduced recently in the multivariate SSA (mSSA) literature. The change point in such a setting corresponds to a change in the underlying spatio-temporal model. As the primary contributions of this work, we develop an algorithm based on CUSUM-statistic to detect such change points in an online fashion. We extend the analysis of CUSUM statistics, traditionally done for the setting of independent observations, to the dependent setting of (multivariate) time series under the spatio-temporal model. Specifically, for a given parameter $h > 0$, our method achieves the following desirable trade-off: when a change happens, it detects it within $O(h)$ time delay on average, while in the absence of change, it does not declare false detection for at least $\exp(\Omega(h))$ time length on average. We conduct empirical experiments using benchmark and synthetic datasets. We find that the proposed method performs competitively or outperforms the state-of-the-art change point detection methods across datasets.
Meta-learning to Improve Pre-training
Authors: Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David K. Duvenaud
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Pre-training (PT) followed by fine-tuning (FT) is an effective method for training neural networks, and has led to significant performance improvements in many domains.  PT can incorporate various design choices such as task and data reweighting strategies, augmentation policies, and noise models, all of which can significantly impact the quality of representations learned. The hyperparameters introduced by these strategies therefore must be tuned appropriately. However, setting the values of these hyperparameters is challenging. Most existing methods either struggle to scale to high dimensions, are too slow and memory-intensive, or cannot be directly applied to the two-stage PT and FT learning process. In this work, we propose an efficient, gradient-based algorithm to meta-learn PT hyperparameters. We formalize the PT hyperparameter optimization problem and propose a novel method to obtain PT hyperparameter gradients by combining implicit differentiation and backpropagation through unrolled optimization. We demonstrate that our method improves predictive performance on two real-world domains. First, we optimize high-dimensional task weighting hyperparameters for multitask pre-training on protein-protein interaction graphs and improve AUROC by up to 3.9%. Second, we optimize a data augmentation neural network for self-supervised PT with SimCLR on electrocardiography data and improve AUROC by up to 1.9%.
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
Authors: Adarsh Barik, Jean Honorio
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: In this paper, we study the problem of fair sparse regression on a biased dataset where bias depends upon a hidden binary attribute. The presence of a hidden attribute adds an extra layer of complexity to the problem by combining sparse regression and clustering with unknown binary labels. The corresponding optimization problem is combinatorial, but we propose a novel relaxation of it as an invex optimization problem. To the best of our knowledge, this is the first invex relaxation for a combinatorial problem. We show that the inclusion of the debiasing/fairness constraint in our model has no adverse effect on the performance. Rather, it enables the recovery of the hidden attribute. The support of our recovered regression parameter vector matches exactly with the true parameter vector. Moreover, we simultaneously solve the clustering problem by recovering the exact value of the hidden attribute for each sample. Our method uses carefully constructed primal dual witnesses to provide theoretical guarantees for the combinatorial problem. To that end, we show that the sample complexity of our method is logarithmic in terms of the dimension of the regression parameter vector.
Probabilistic Margins for Instance Reweighting in Adversarial Training
Authors: qizhou wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights. However, existing methods measuring the closeness are not very reliable: they are discrete and can take only a few values, and they are path-dependent, i.e., they may change given the same start and end points with different attack paths. In this paper, we propose three types of probabilistic margin (PM), which are continuous and path-independent, for measuring the aforementioned closeness and reweighing adversarial data. Specifically, a PM is defined as the difference between two estimated class-posterior probabilities, e.g., such a probability of the true label minus the probability of the most confusing label given some natural data. Though different PMs capture different geometric properties, all three PMs share a negative correlation with the vulnerability of data: data with larger/smaller PMs are safer/riskier and should have smaller/larger weights. Experiments demonstrated that PMs are reliable and PM-based reweighting methods outperformed state-of-the-art counterparts.
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
Authors: Laetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte, Gilles Gasso
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the marginal conditions are relaxed (using weighted penalties in lieu of equality) and no additional regularization is enforced on the OT plan. In this context, we show that the corresponding optimization problem can be reformulated as a non-negative penalized linear regression problem. This reformulation allows us to propose novel algorithms inspired from inverse problems and nonnegative matrix factorization. In particular, we consider majorization-minimization which leads in our setting to efficient multiplicative updates for a variety of penalties. Furthermore, we derive for the first time an efficient algorithm to compute the regularization path of UOT with quadratic penalties. The proposed algorithm provides a continuity of piece-wise linear OT plans converging to the solution of balanced OT (corresponding to infinite penalty weights). We perform several numerical experiments on simulated and real data illustrating the new algorithms, and provide a detailed discussion about more sophisticated optimization tools that can further be used to solve OT problems thanks to our reformulation.
The Difficulty of Passive Learning in Deep Reinforcement Learning
Authors: Georg Ostrovski, Pablo Samuel Castro, Will Dabney
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Learning to act from observational data without active environmental interaction is a well-known challenge in Reinforcement Learning (RL). Recent approaches involve constraints on the learned policy or conservative updates, preventing strong deviations from the state-action distribution of the dataset. Although these methods are evaluated using non-linear function approximation, theoretical justifications are mostly limited to the tabular or linear cases. Given the impressive results of deep reinforcement learning, we argue for a need to more clearly understand the challenges in this setting.In the vein of Held & Hein's classic 1963 experiment, we propose the "tandem learning" experimental paradigm which facilitates our empirical analysis of the difficulties in offline reinforcement learning. We identify function approximation in conjunction with fixed data distributions as the strongest factors, thereby extending but also challenging hypotheses stated in past work. Our results provide relevant insights for offline deep reinforcement learning, while also shedding new light on phenomena observed in the online case of learning control.
Intriguing Properties of Vision Transformers
Authors: Muhammad Muzammal Naseer, Kanchana Ranasinghe, Salman H Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Vision transformers (ViT) have demonstrated impressive performance across numerous machine vision tasks. These models are based on multi-head self-attention mechanisms that can flexibly attend to a sequence of image patches to encode contextual cues. An important question is how such flexibility (in attending image-wide context conditioned on a given patch) can facilitate handling nuisances in natural images e.g., severe occlusions, domain shifts, spatial permutations, adversarial and natural perturbations. We systematically study this question via an extensive set of experiments encompassing three ViT families and provide comparisons with a high-performing convolutional neural network (CNN). We show and analyze the following intriguing properties of ViT: (a)Transformers are highly robust to severe occlusions, perturbations and domain shifts, e.g., retain as high as 60% top-1 accuracy on ImageNet even after randomly occluding 80% of the image content. (b)The robustness towards occlusions is not due to texture bias, instead we show that ViTs are significantly less biased towards local textures, compared to CNNs. When properly trained to encode shape-based features, ViTs demonstrate shape recognition capability comparable to that of human visual system, previously unmatched in the literature. (c)Using ViTs to encode shape representation leads to an interesting consequence of accurate semantic segmentation without pixel-level supervision. (d)Off-the-shelf features from a single ViT model can be combined to create a feature ensemble,  leading to high accuracy rates across a range of classification datasets in both traditional and few-shot learning paradigms.  We show effective features of ViTs are due to flexible and dynamic receptive fields possible via self-attention mechanisms. Our code will be publicly released.
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
Authors: Benyuan Sun, Hongxing Huo, YI YANG, Bo Bai
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The burst of applications empowered by massive data have aroused unprecedented privacy concerns in AI society. Currently, data confidentiality protection has been one core issue during deep model training. Federated Learning (FL), which enables privacy-preserving training across multiple silos, gained rising popularity for its parameter-only communication. However, previous works have shown that FL revealed a significant performance drop if the data distributions are heterogeneous among different clients, especially when the clients have cross-domain characteristic, such as traffic, aerial and in-door. To address this challenging problem, we propose a novel idea, PartialFed, which loads a subset of the global model’s parameters rather than loading the entire model used in most previous works. We first validate our algorithm with manually decided loading strategies inspired by various expert priors, named PartialFed-Fix. Then we develop PartialFed-Adaptive, which automatically selects personalized loading strategy for each client. The superiority of our algorithm is proved by demonstrating the new state-of-the-art results on cross-domain federated classification and detection. In particular, solely by initializing a small fraction of layers locally, we improve the performance of FedAvg on Office-Home and UODB by 4.88% and 2.65%, respectively. Further studies show that the adaptive strategy performs significantly better on domains with large deviation, e.g. improves AP50 by 4.03% and 4.89% on aerial and medical image detection compared to FedAvg.
Adaptive Diffusion in Graph Neural Networks
Authors: Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The success of graph neural networks (GNNs) largely relies on the process of aggregating information from neighbors defined by the input graph structures. Notably, message passing based GNNs, e.g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion. However, the neighborhood size in GDC is manually tuned for each graph by conducting grid search over the validation set, making its generalization practically limited. To address this issue, we propose the adaptive diffusion convolution (ADC) strategy to automatically learn the optimal neighborhood size from the data. Furthermore, we break the conventional assumption that all GNN layers and feature channels (dimensions) should use the same neighborhood for propagation. We design strategies to enable ADC to learn a dedicated propagation neighborhood for each GNN layer and each feature channel, making the GNN architecture fully coupled with graph structures---the unique property that differs GNNs from traditional neural networks. By directly plugging ADC into existing GNNs, we observe consistent and significant outperformance over both GDC and their vanilla versions across various datasets, demonstrating the improved model capacity brought by automatically learning unique neighborhood size per layer and per channel in GNNs.
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits
Authors: Orestis Papadigenopoulos, Constantine Caramanis
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: A recent line of research focuses on the study of stochastic multi-armed bandits (MAB), in the case where temporal correlations of specific structure are imposed between the player's actions and the reward distributions of the arms. These correlations lead to (sub-)optimal solutions that exhibit interesting dynamical patterns -- a phenomenon that yields new challenges both from an algorithmic as well as a learning perspective. In this work, we extend the above direction to a combinatorial semi-bandit setting and study a variant of stochastic MAB, where arms are subject to matroid constraints and each arm becomes unavailable (blocked) for a fixed number of rounds after each play. A natural common generalization of the state-of-the-art for blocking bandits, and that for matroid bandits, only guarantees a $\frac{1}{2}$-approximation for general matroids. In this paper we develop the novel technique of correlated (interleaved) scheduling, which allows us to obtain a polynomial-time $(1 - \frac{1}{e})$-approximation algorithm (asymptotically and in expectation) for any matroid. Along the way, we discover an interesting connection to a variant of Submodular Welfare Maximization, for which we provide (asymptotically) matching upper and lower approximability bounds. In the case where the mean arm rewards are unknown, our technique naturally decouples the scheduling from the learning problem, and thus allows to control the $(1-\frac{1}{e})$-approximate regret of a UCB-based adaptation of our online algorithm.
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
Authors: Yi Sui, Ga Wu, Scott Sanner
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Explaining the influence of training data on deep neural network predictions is a critical tool for debugging models through data curation.  A recent tractable and appealing approach for this task was provided via the concept of Representer Point Selection (RPS), i.e. a method the leverages the dual form of $l_2$ regularized optimization in the last layer of the neural network to identify the contribution of training points to the prediction.  However, two key drawbacks of RPS are that they (i) lead to disagreement between the originally trained network and the RP regularized network modification and (ii) often yield a static ranking of training data for the same class, independent of the data being classified.  Inspired by the RPS approach, we propose an alternative method based on a local Jacobian Taylor expansion (LJE) of the Jacobian.We empirically compared RPS-LJE with the original RPS-$l_2$ on image classification (with ResNet), text classification recurrent neural networks (with Bi-LSTM), and tabular classification (with XGBoost) tasks.Quantitatively, we show that RPS-LJE slightly outperforms RPS-$l_2$ and other state-of-the-art data explanation methods by up to 3\% on a data debugging task.  Qualitatively, we observe that RPS-LJE provides individualized explanations for each test data point rather than the class-specific static ranking of points in the original approach.  Overall, RPS-LJE represents a novel approach to RPS that provides a  powerful tool for data-oriented explanation and debugging.