pwc-archive/papers-with-abstracts huggingface dataset:
Rethinking the Variational Interpretation of Accelerated Optimization Methods
Authors: Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12

The continuous-time model of Nesterov's momentum provides a thought-provoking perspective for understanding the nature of the acceleration phenomenon in convex optimization. One of the main ideas in this line of research comes from the field of classical mechanics and proposes to link Nesterov's trajectory to the solution of a set of Euler-Lagrange equations relative to the so-called Bregman Lagrangian. In the last years, this approach led to the discovery of many new (stochastic) accelerated algorithms and provided a solid theoretical foundation for the design of structure-preserving accelerated methods. In this work, we revisit this idea and provide an in-depth analysis of the action relative to the Bregman Lagrangian from the point of view of calculus of variations. Our main finding is that, while Nesterov's method is a stationary point for the action, it is often not a minimizer but instead a saddle point for this functional in the space of differentiable curves. This finding challenges the main intuition behind the variational interpretation of Nesterov's method and provides additional insights into the intriguing geometry of accelerated paths.
Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters
Authors: Arvind Mahankali, David Woodruff
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12

We study linear and kernel classification in the streaming model. For linear classification, we improve upon the algorithm of (Tai, et al. 2018), which solves the $\ell_1$ point query problem on the optimal weight vector $w_* \in \mathbb{R}^d$ in sublinear space. We first give an algorithm solving the more difficult $\ell_2$ point query problem on $w_*$, also in sublinear space. We also give an algorithm which solves the $\ell_2$ heavy hitter problem on $w_*$, in sublinear space and running time. Finally, we give an algorithm which can $\textit{deterministically}$ solve the $\ell_1$ point query problem on $w_*$, with sublinear space improving upon that of (Tai, et al. 2018). For kernel classification, if $w_* \in \mathbb{R}^{d^p}$ is the optimal weight vector classifying points in the stream according to their $p^{th}$-degree polynomial kernel, then we give an algorithm solving the $\ell_2$ point query problem on $w_*$ in $\text{poly}(\frac{p \log d}{\varepsilon})$ space, and an algorithm solving the $\ell_2$ heavy hitter problem in $\text{poly}(\frac{p \log d}{\varepsilon})$ space and running time. Note that our space and running time are polynomial in $p$, making our algorithms well-suited to high-degree polynomial kernels and the Gaussian kernel (approximated by the polynomial kernel of degree $p = \Theta(\log T)$).
Modified Frank Wolfe in Probability Space
Authors: Carson Kent, Jiajin Li, Jose Blanchet, Peter W. Glynn
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Variational Inference

We propose a novel Frank-Wolfe (FW) procedure for the optimization of infinite-dimensional functionals of probability measures - a task which arises naturally in a wide range of areas including statistical learning (e.g. variational inference) and artificial intelligence (e.g. generative adversarial networks). Our FW procedure takes advantage of Wasserstein gradient flows and strong duality results recently developed in Distributionally Robust Optimization so that gradient steps (in the Wasserstein space) can be efficiently computed using finite-dimensional, convex optimization methods. We show how to choose the step sizes in order to guarantee exponentially fast iteration convergence, under mild assumptions on the functional to optimize. We apply our algorithm to a range of functionals arising from applications in nonparametric estimation.
Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos
Authors: Reuben Tan, Bryan Plummer, Kate Saenko, Hailin Jin, Bryan Russell
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12

We introduce the task of spatially localizing narrated interactions in videos. Key to our approach is the ability to learn to spatially localize interactions with self-supervision on a large corpus of videos with accompanying transcribed narrations. To achieve this goal, we propose a multilayer cross-modal attention network that enables effective optimization of a contrastive loss during training. We introduce a divided strategy that alternates between computing inter- and intra-modal attention across the visual and natural language modalities, which allows effective training via directly contrasting the two modalities' representations. We demonstrate the effectiveness of our approach by self-training on the HowTo100M instructional video dataset and evaluating on a newly collected dataset of localized described interactions in the YouCook2 dataset. We show that our approach outperforms alternative baselines, including shallow co-attention and full cross-modal attention. We also apply our approach to grounding phrases in images with weak supervision on Flickr30K and show that stacking multiple attention layers is effective and, when combined with a word-to-region loss, achieves state of the art on recall-at-one and pointing hand accuracies.
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective
Authors: Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, HengShu Zhu, Hui Xiong
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Variational Inference

Most topic modeling approaches are based on the bag-of-words assumption, where each word is required to be conditionally independent in the same document. As a result, both of the generative story and the topic formulation have totally ignored the semantic dependency among words, which is important for improving the semantic comprehension and model interpretability. To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM). Specifically, in GNTM, a well-defined probabilistic generative story is designed to model both the graph structure and word sets with multinomial distributions on the vocabulary and word dependency edge set as the topics. Meanwhile, a Neural Variational Inference (NVI) approach is proposed to learn our model with graph neural networks to encode the document graphs. Besides, we theoretically demonstrate that Latent Dirichlet Allocation (LDA) can be derived from GNTM as a special case with similar objective functions. Finally, extensive experiments on four benchmark datasets have clearly demonstrated the effectiveness and interpretability of GNTM compared with state-of-the-art baselines.
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights
Authors: Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12

We examine an adaptive learning framework for nonatomic congestion games where the players' cost functions may be subject to exogenous fluctuations (e.g., due to disturbances in the network, variations in the traffic going through a link). In this setting, the popular multiplicative/ exponential weights algorithm enjoys an $\mathcal{O}(1/\sqrt{T})$ equilibrium convergence rate; however, this rate is suboptimal in static environments---i.e., when the network is not subject to randomness. In this static regime, accelerated algorithms achieve an $\mathcal{O}(1/T^{2})$ convergence speed, but they fail to converge altogether in stochastic problems. To fill this gap, we propose a novel,  adaptive exponential weights method---dubbed AdaWeight---that seamlessly interpolates between the $\mathcal{O}(1/T^{2})$  and $\mathcal{O}(1/\sqrt{T})$ rates in the static and stochastic regimes respectively. Importantly, this "best-of-both-worlds" guarantee does not require any prior knowledge of the problem's parameters or tuning by the optimizer; in addition, the method's convergence speed depends subquadratically on the size of the network (number of vertices and edges), so it scales gracefully to large, real-life urban networks.
Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent
Authors: Xingchao Liu, Xin Tong, Qiang Liu
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Diversity

Finding diverse and representative Pareto solutions from the Pareto front is a key challenge in multi-objective optimization (MOO). In this work, we propose a novel gradient-based algorithm for profiling Pareto front by using Stein variational gradient descent (SVGD). We also provide a counterpart of our method based on Langevin dynamics. Our methods iteratively update a set of points in a parallel fashion to push them towards the Pareto front using multiple gradient descent, while encouraging the diversity between the particles by using the repulsive force mechanism in SVGD, or diffusion noise in Langevin dynamics. Compared with existing gradient-based methods that require predefined preference functions, our method can work efficiently in high dimensional problems, and can obtain more diverse solutions evenly distributed in the Pareto front. Moreover, our methods are theoretically guaranteed to converge to the Pareto front. We demonstrate the effectiveness of our method, especially the SVGD algorithm, through extensive experiments, showing its superiority over existing gradient-based algorithms.
Meta-Learning via Learning with Distributed Memory
Authors: Sudarshan Babu, Pedro Savarese, Michael Maire
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Few-Shot Semantic Segmentation, Meta-Learning, Semantic Segmentation

We demonstrate that efficient meta-learning can be achieved via end-to-end training of deep neural networks with memory distributed across layers.  The persistent state of this memory assumes the entire burden of guiding task adaptation.  Moreover, its distributed nature is instrumental in orchestrating adaptation.  Ablation experiments demonstrate that providing relevant feedback to memory units distributed across the depth of the network enables them to guide adaptation throughout the entire network.  Our results show that this is a successful strategy for simplifying meta-learning -- often cast as a bi-level optimization problem -- to standard end-to-end training, while outperforming gradient-based, prototype-based, and other memory-based meta-learning strategies.  Additionally, our adaptation strategy naturally handles online learning scenarios with a significant delay between observing a sample and its corresponding label -- a setting in which other approaches struggle.  Adaptation via distributed memory is effective across a wide range of learning tasks, ranging from classification to online few-shot semantic segmentation.
Adversarial Reweighting for Partial Domain Adaptation
Authors: Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Domain Adaptation, Partial Domain Adaptation

Partial domain adaptation (PDA) has gained much attention due to its practical setting. The current PDA methods usually adapt the feature extractor by aligning the target and reweighted source domain distributions. In this paper, we experimentally find that the feature adaptation by the reweighted distribution alignment in some state-of-the-art PDA methods is not robust to the ``noisy'' weights of source domain data, leading to negative domain transfer on some challenging benchmarks. To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data. Based on this idea, we propose a training algorithm that alternately updates the parameters of the network and optimizes the weights of source domain data. Extensive experiments show that our method achieves state-of-the-art results on the benchmarks of ImageNet-Caltech, Office-Home, VisDA-2017, and DomainNet. Ablation studies also confirm the effectiveness of our approach.
Graph Adversarial Self-Supervised Learning
Authors: Longqi Yang, Liangliang Zhang, Wenjing Yang
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Graph Classification, Self-Supervised Learning

This paper studies a long-standing problem of learning the representations of a whole graph without human supervision. The recent self-supervised learning methods train models to be invariant to the transformations (views) of the inputs. However, designing these views requires the experience of human experts. Inspired by adversarial training, we propose an adversarial self-supervised learning (\texttt{GASSL}) framework for learning unsupervised representations of graph data without any handcrafted views. \texttt{GASSL} automatically generates challenging views by adding perturbations to the input and are adversarially trained with respect to the encoder. Our method optimizes the min-max problem and utilizes a gradient accumulation strategy to accelerate the training process. Experimental on ten graph classification datasets show that the proposed approach is superior to state-of-the-art self-supervised learning baselines, which are competitive with supervised models.
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models
Authors: keunseo kim, JunCheol Shin, Heeyoung Kim
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Several out-of-distribution (OOD) detection scores have been recently proposed for deep generative models because the direct use of the likelihood threshold for OOD detection has been shown to be problematic. In this paper, we propose a new OOD score based on a Bayesian hypothesis test called the locally most powerful Bayesian test (LMPBT). The LMPBT is locally most powerful in that the alternative hypothesis (the representative parameter for the OOD sample) is specified to maximize the probability that the Bayes factor exceeds the evidence threshold in favor of the alternative hypothesis provided that the parameter specified under the alternative hypothesis is in the neighborhood of the parameter specified under the null hypothesis. That is, under this neighborhood parameter condition, the test with the proposed alternative hypothesis maximizes the probability of correct detection of OOD samples. We also propose numerical strategies for more efficient and reliable computation of the LMPBT for practical application to deep generative models. Evaluations conducted of the OOD detection performance of the LMPBT on various benchmark datasets demonstrate its superior performance over existing OOD detection methods.
Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable
Authors: Tian-Zuo Wang, Zhi-Hua Zhou
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12

In many real tasks, it is generally desired to study the causal effect on a specific target (response variable) only, with no need to identify the thorough causal effects involving all variables. In this paper, we attempt to identify such effects by a few active interventions where only the response variable is observable. This task is challenging because the causal graph is unknown and even there may exist latent confounders. To learn the necessary structure for identifying the effects, we provide the graphical characterization that allows us to efficiently estimate all possible causal effects in a partially mixed ancestral graph (PMAG) by generalized back-door criterion. The characterization guides learning a local structure with the interventional data. Theoretical analysis and empirical studies validate the effectiveness and efficiency of our proposed approach.
Deep Extended Hazard Models for Survival Analysis
Authors: Qixian Zhong, Jonas W. Mueller, Jane-Ling Wang
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Survival Analysis

Unlike standard prediction tasks, survival analysis requires modeling right censored data, which must be treated with care. While deep neural networks excel in traditional supervised learning, it remains unclear how to best utilize these models in survival analysis. A key question asks which data-generating assumptions of traditional survival models should be retained and which should be made more flexible via the function-approximating capabilities of neural networks. Rather than estimating the survival function targeted by most existing methods, we introduce a Deep Extended Hazard (DeepEH) model to provide a flexible and general framework for deep survival analysis. The extended hazard model includes the conventional Cox proportional hazards  and accelerated failure time models as special cases, so DeepEH subsumes the popular Deep Cox proportional hazard (DeepSurv) and Deep Accelerated Failure Time (DeepAFT) models. We additionally provide theoretical support for the proposed DeepEH model by establishing consistency and convergence rate of the survival function estimator, which underscore the attractive feature that deep learning is able to detect low-dimensional structure of data in high-dimensional space. Numerical experiments also provide evidence that the proposed methods outperform existing statistical and deep learning approaches to survival analysis.
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework
Authors: Shun Lu, Jixiang Li, Jianchao Tan, Sen yang, Ji Liu
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Neural Architecture Search

Predictor-based Neural Architecture Search (NAS) continues to be an important topic because it aims to mitigate the time-consuming search procedure of traditional NAS methods. A promising performance predictor determines the quality of final searched models in predictor-based NAS methods. Most existing predictor-based methodologies train model-based predictors under a proxy dataset setting, which may suffer from the accuracy decline and the generalization problem, mainly due to their poor abilities to represent spatial topology information of the graph structure data. Besides the poor encoding for spatial topology information, these works did not take advantage of the temporal information such as historical evaluations during training. Thus, we propose a Transformer-based NAS performance predictor, associated with a Laplacian matrix based positional encoding strategy, which better represents topology information and achieves better performance than previous state-of-the-art methods on NAS-Bench-101, NAS-Bench-201, and DARTS search space. Furthermore, we also propose a self-evolution framework that can fully utilize temporal information as guidance. This framework iteratively involves the evaluations of previously predicted results as constraints into current optimization iteration, thus further improving the performance of our predictor. Such framework is model-agnostic, thus can enhance performance on various backbone structures for the prediction task. Our proposed method helped us rank 2nd among all teams in CVPR 2021 NAS Competition Track 2: Performance Prediction Track.
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
Authors: Jiachen Sun, Yulong Cao, Christopher B. Choy, Zhiding Yu, Anima Anandkumar, Zhuoqing Morley Mao, Chaowei Xiao
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Adversarial Robustness, Autonomous Driving, Self-Supervised Learning

3D point cloud data is increasingly used in safety-critical applications such as autonomous driving. Thus, the robustness of 3D deep learning models against adversarial attacks becomes a major consideration. In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds with adversarial training. Specifically, we study MLP-based (PointNet), convolution-based (DGCNN), and transformer-based (PCT) 3D architectures. Through extensive experimentation, we demonstrate that appropriate applications of self-supervision can significantly enhance the robustness in 3D point cloud recognition, achieving considerable improvements compared to the standard adversarial training baseline. Our analysis reveals that local feature learning is desirable for adversarial robustness in point clouds since it limits the adversarial propagation between the point-level input perturbations and the model's final output. This insight also explains the success of DGCNN and the jigsaw proxy task in achieving stronger 3D adversarial robustness.
Representing Hyperbolic Space Accurately using Multi-Component Floats
Authors: Tao Yu, Christopher M. De Sa
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12

Hyperbolic space is particularly useful for embedding data with hierarchical structure; however, representing hyperbolic space with ordinary floating-point numbers greatly affects the performance due to its \emph{ineluctable} numerical errors. Simply increasing the precision of floats fails to solve the problem and incurs a high computation cost for simulating greater-than-double-precision floats on hardware such as GPUs, which does not support them. In this paper, we propose a simple, feasible-on-GPUs, and easy-to-understand solution for numerically accurate learning on hyperbolic space. We do this with a new approach to represent hyperbolic space using multi-component floating-point (MCF) in the Poincar{\'e} upper-half space model. Theoretically and experimentally we show our model has small numerical error, and on embedding tasks across various datasets, models represented by multi-component floating-points gain more capacity and run significantly faster on GPUs than prior work.
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes
Authors: HyunJi Nam, Scott Fleming, Emma Brunskill
Date: 2021-12-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Reinforcement Learning (RL)

Many real-world problems that require making optimal sequences of decisions under uncertainty involve costs when the agent wishes to obtain information about its environment. We design and analyze algorithms for reinforcement learning (RL) in Action-Contingent Noiselessly Observable MDPs (ACNO-MDPs), a special class of POMDPs in which the agent can choose to either (1) fully observe the state at a cost and then act; or (2) act without any immediate observation information, relying on past observations to infer the underlying state. ACNO-MDPs arise frequently in important real-world application domains like healthcare, in which clinicians must balance the value of information gleaned from medical tests (e.g., blood-based biomarkers) with the costs of gathering that information (e.g., the costs of labor and materials required to administer such tests). We develop a PAC RL algorithm for tabular ACNO-MDPs that provides substantially tighter bounds, compared to generic POMDP-RL algorithms, on the total number of episodes exhibiting worse than near-optimal performance. For continuous-state, continuous-action ACNO-MDPs, we propose a novel method of incorporating observation information that, when coupled with modern RL algorithms, yields significantly faster learning compared to other POMDP-RL algorithms in several simulated environments.