NeurIPS 2020

1,898 papers

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization

Sam Hopkins, Jerry Li, Fred Zhang

Predictive Information Accelerates Learning in RL

Kuang-Huei Lee, Ian Fischer, Anthony Liu, Yijie Guo, Honglak Lee

Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits

Siwei Wang, Longbo Huang, John C.S. Lui, John C. S. Lui

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning

Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet

Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning

Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, Yuk Ying Chung

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Jihoon Tack, Sangwoo Mo, Jongheon Jeong, Jinwoo Shin

A shooting formulation of deep learning

Francois-Xavier Vialard, Roland Kwitt, Susan Wei, Marc Niethammer

Distributional Robustness with IPMs and links to Regularization and GANs

Hisham Husain

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

yaxing wang, Lu Yu, Joost van de Weijer

Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity

Gonçalo Correia, Vlad Niculae, Wilker Aziz, Andre Martins

Online Decision Based Visual Tracking via Reinforcement Learning

ke Song, Wei Zhang, Ran Song, Yibin Li

Softmax Deep Double Deterministic Policy Gradients

Ling Pan, Qingpeng Cai, Longbo Huang

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration

Andrea Zanette, Alessandro Lazaric, Mykel J Kochenderfer, Emma Brunskill

Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery

Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong

Task-agnostic Exploration in Reinforcement Learning

Xuezhou Zhang, Yuzhe Ma, Adish Singla

In search of robust measures of generalization

Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero

MCUNet: Tiny Deep Learning on IoT Devices

Ji Lin, Wei-Ming Chen, Yujun Lin, john cohn, Chuang Gan

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web

Zhenwei Dai, Anshumali Shrivastava

Manifold structure in graph embeddings

Patrick Rubin-Delanchy

Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations

Alexander Ritchie, Robert A Vandermeulen, Clayton Scott

Deep active inference agents using Monte-Carlo methods

Zafeirios Fountas, Noor Sajid, Pedro Mediano, Karl Friston

Hierarchical Granularity Transfer Learning

Shaobo Min, Hongtao Xie, Hantao Yao, Xuran Deng, Zheng-Jun Zha

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models

Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

Proximal Mapping for Deep Regularization

Mao Li, Yingyi Ma, Xinhua Zhang

Sparse Learning with CART

Jason Klusowski

A polynomial-time algorithm for learning nonparametric causal graphs

Ming Gao, Yi Ding, Bryon Aragam

Look-ahead Meta Learning for Continual Learning

Gunshi Gupta, Karmesh Yadav, Liam Paull

Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning

Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations

Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck

Deep Smoothing of the Implied Volatility Surface

Damien Ackerer, Natasa Tagasovska, Thibault Vatter

Improving robustness against common corruptions by covariate shift adaptation

Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel

Object-Centric Learning with Slot Attention

Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold

Learning to solve TV regularised problems with unrolled algorithms

Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau

MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures

Jeong Un Ryu, JaeWoong Shin, Hae Beom Lee, Sung Ju Hwang

Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

Kaiyi Ji, Jason Lee, Yingbin Liang, H. Vincent Poor

Adapting to Misspecification in Contextual Bandits

Dylan J Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert

Coresets for Robust Training of Deep Neural Networks against Noisy Labels

Baharan Mirzasoleiman, Kaidi Cao, Jure Leskovec

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images

Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey

An implicit function learning approach for parametric modal regression

Yangchen Pan, Ehsan Imani, Amir-massoud Farahmand, Martha White

JAX MD: A Framework for Differentiable Physics

Samuel Schoenholz, Ekin Dogus Cubuk

Compositional Generalization by Learning Analytical Expressions

Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin

Joints in Random Forests

Alvaro Correia, Robert Peharz, Cassio P de Campos

Adversarial robustness via robust low rank representations

Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan

Winning the Lottery with Continuous Sparsification

Pedro Savarese, Hugo Silva, Michael Maire

Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses

Ronald (James) Cotton, Fabian H. Sinz, Andreas Tolias

Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming

Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama, Ichiro Takeuchi

Smooth And Consistent Probabilistic Regression Trees

Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Eric Gaussier

Instance-based Generalization in Reinforcement Learning

Martin Bertran, Natalia Martinez, Mariano Phielipp, Guillermo Sapiro

Fairness constraints can help exact inference in structured prediction

Kevin Bello, Jean Honorio

Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID

Yixiao Ge, Feng Zhu, Dapeng Chen, Rui Zhao, hongsheng Li

RD$^2$: Reward Decomposition with Representation Decomposition

Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang

TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning

Han Cai, Chuang Gan, Ligeng Zhu, Song Han

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Austin Tripp, Erik Daxberger, José Miguel Hernández-Lobato

RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, JINYU YANG

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato, Cheng Zhang

GreedyFool: Distortion-Aware Sparse Adversarial Attack

Xiaoyi Dong, Dongdong Chen, Jianmin Bao, Chuan Qin, Lu Yuan

Metric-Free Individual Fairness in Online Learning

Yahav Bechavod, Christopher Jung, Steven Z. Wu

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner

Stage-wise Conservative Linear Bandits

Ahmadreza Moradipari, CHRISTOS THRAMPOULIDIS, Mahnoosh Alizadeh

On Adaptive Distance Estimation

Yeshwanth Cherapanamjeri, Jelani Nelson

SnapBoost: A Heterogeneous Boosting Machine

Thomas Parnell, Andreea Anghel, Małgorzata Łazuka, Nikolas Ioannou, Sebastian Kurella

Modeling and Optimization Trade-off in Meta-learning

Katelyn Gao, Ozan Sener

LoCo: Local Contrastive Representation Learning

Yuwen Xiong, Mengye Ren, Raquel Urtasun

Fine-Grained Dynamic Head for Object Detection

Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun

Soft Contrastive Learning for Visual Localization

Janine Thoma, Danda Pani Paudel, Luc V Gool, Luc V. Gool

Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information

Genevieve Flaspohler, Nicholas A. Roy, John W. Fisher III

Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function

Quoc Tran Dinh, Deyi Liu, Lam Nguyen

Fair Performance Metric Elicitation

Gaurush Hiranandani, Harikrishna Narasimhan, Sanmi Koyejo

Phase retrieval in high dimensions: Statistical and computational phase transitions

Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets

Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross

The Smoothed Possibility of Social Choice

Lirong Xia

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain

Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition

Ben Adlam, Jeffrey Pennington

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding

Victor Veitch, Anisha Zaveri

Learning Some Popular Gaussian Graphical Models without Condition Number Bounds

Jonathan Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra

Discovering conflicting groups in signed networks

Ruo-Chun Tzeng, Bruno Ordozgoiti, Aristides Gionis

Robust, Accurate Stochastic Optimization for Variational Inference

Akash Kumar Dhaka, Alejandro Catalina, Michael R Andersen, Måns Magnusson, Jonathan Huggins

Provably Consistent Partial-Label Learning

Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu

SCOP: Scientific Control for Reliable Neural Network Pruning

Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing XU

Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing

Vishaal Krishnan, Abed AlRahman Al Makdah, Fabio Pasqualetti

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error

Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni

General Transportability of Soft Interventions: Completeness Results

Juan Correa, Elias Bareinboim

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks

Ryo Karakida, Kazuki Osawa

SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm

Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation

KwanYong Park, Sanghyun Woo, Inkyu Shin, In So Kweon

Estimating Fluctuations in Neural Representations of Uncertain Environments

Sahand Farhoodi, Mark Plitt, Lisa Giocomo, Uri Eden

Incorporating BERT into Parallel Sequence Decoding with Adapters

Junliang Guo, Zhirui Zhang, Linli Xu, Hao-Ran Wei, Boxing Chen

Learning Compositional Rules via Neural Program Synthesis

Maxwell Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden M Lake

Unsupervised Text Generation by Learning from Search

Jingjing Li, Zichao Li, Lili Mou, Xin Jiang, Michael Lyu

A mathematical model for automatic differentiation in machine learning

Jérôme Bolte, Edouard Pauwels

Efficient Projection-free Algorithms for Saddle Point Problems

Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu

An Optimal Elimination Algorithm for Learning a Best Arm

Avinatan Hassidim, Ron Kupfer, Yaron Singer

Generalized Leverage Score Sampling for Neural Networks

Jason Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, zheng Yu

Non-Stochastic Control with Bandit Feedback

Paula Gradu, John Hallman, Elad Hazan

High-Dimensional Sparse Linear Bandits

Botao Hao, Tor Lattimore, Mengdi Wang

Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control

Yaofeng Desmond Zhong, Naomi Leonard

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

Monotone operator equilibrium networks

Ezra Winston, J. Zico Kolter