NeurIPS 2022

2,834 papers

Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering

Yaming Yang, Ziyu Guan, Zhe Wang, Wei Zhao, Cai Xu

Function Classes for Identifiable Nonlinear Independent Component Analysis

Simon Buchholz, Michel Besserve, Bernhard Schölkopf

Reinforcement Learning with Neural Radiance Fields

Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint

When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn

Recursive Reasoning in Minimax Games: A Level k Gradient Play Method

Zichu Liu, Lacra Pavel

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

Ming Ding, Wendi Zheng, Wenyi Hong, Jie Tang

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets

Nikolaj Thams, Michael Oberst, David Sontag

Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs

Ruijie Wang, Zheng Li, Dachun Sun, Shengzhong Liu, Jinning Li

Dynamic Sparse Network for Time Series Classification: Learning What to “See”

Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy

Invertible Monotone Operators for Normalizing Flows

Byeongkeun Ahn, Chiyoon Kim, Youngjoon Hong, Hyunwoo J Kim

Latent Planning via Expansive Tree Search

Robert Gieselmann, Florian T. Pokorny

Semi-infinitely Constrained Markov Decision Processes

Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang

Bounding and Approximating Intersectional Fairness through Marginal Fairness

Mathieu Molina, Patrick Loiseau

Poisson Flow Generative Models

Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola

Learning Chaotic Dynamics in Dissipative Systems

Zongyi Li, Miguel Liu-Schiaffini, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu

Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation

JoonHo Jang, Byeonghu Na, Dong Hyeok Shin, Mingi Ji, Kyungwoo Song

SwinTrack: A Simple and Strong Baseline for Transformer Tracking

Liting Lin, Heng Fan, Zhipeng Zhang, Yong Xu, Haibin Ling

Trajectory Inference via Mean-field Langevin in Path Space

Lénaïc Chizat, Stephen Zhang, Matthieu Heitz, Geoffrey Schiebinger

Robust Bayesian Regression via Hard Thresholding

Zheyi Fan, Zhaohui Li, Qingpei Hu

TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training

Yulong Liu, Guibo Zhu, Bin Zhu, Qi Song, Guojing Ge

Wasserstein Logistic Regression with Mixed Features

Aras Selvi, Mohammad Reza Belbasi, Martin Haugh, Wolfram Wiesemann

Symmetry Teleportation for Accelerated Optimization

Bo Zhao, Nima Dehmamy, Robin Walters, Rose Yu

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Shoufa Chen, Chongjian GE, Zhan Tong, Jiangliu Wang, Yibing Song

A Variant of Anderson Mixing with Minimal Memory Size

Fuchao Wei, Chenglong Bao, Yang Liu, Guangwen Yang

Sharing Knowledge for Meta-learning with Feature Descriptions

Tomoharu Iwata, Atsutoshi Kumagai

HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis

Sang-Hoon Lee, Seung-Bin Kim, Ji-Hyun Lee, Eunwoo Song, Min-Jae Hwang

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Vidhi Lalchand, Wessel Bruinsma, David Burt, Carl Edward Rasmussen

Fast Instrument Learning with Faster Rates

Ziyu Wang, Yuhao Zhou, Jun Zhu

When Do Flat Minima Optimizers Work?

Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J Kusner

Provable Generalization of Overparameterized Meta-learning Trained with SGD

Yu Huang, Yingbin Liang, Longbo Huang

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A Osborne

Non-stationary Bandits with Knapsacks

Shang Liu, Jiashuo Jiang, Xiaocheng Li

Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

Muning Wen, Jakub Kuba, Runji Lin, Weinan Zhang, Ying Wen

Understanding Square Loss in Training Overparametrized Neural Network Classifiers

Tianyang Hu, Jun Wang, Wenjia Wang, Zhenguo Li

Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds

Junsheng Zhou, Baorui Ma, Yu-Shen Liu, Yi Fang, Zhizhong Han

Robust Testing in High-Dimensional Sparse Models

Anand Jerry George, Clément L Canonne

Discovered Policy Optimisation

Chris Lu, Jakub Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt

Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games

Ioannis Anagnostides, Gabriele Farina, Ioannis Panageas, Tuomas Sandholm

Self-Supervised Visual Representation Learning with Semantic Grouping

Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, Xiaojuan Qi

On the Identifiability of Nonlinear ICA: Sparsity and Beyond

Yujia Zheng, Ignavier Ng, Kun Zhang

Spherization Layer: Representation Using Only Angles

Hoyong Kim, kangil kim

On global convergence of ResNets: From finite to infinite width using linear parameterization

Raphaël Barboni, Gabriel Peyré, Francois-Xavier Vialard

Learning Enhanced Representation for Tabular Data via Neighborhood Propagation

Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao

Locally Hierarchical Auto-Regressive Modeling for Image Generation

Tackgeun You, Saehoon Kim, Chiheon Kim, Doyup Lee, Bohyung Han

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, Christopher Re

Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang

On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

Vincent Szolnoky, Viktor Andersson, Balazs Kulcsar, Rebecka Jörnsten

Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching

Kelvin Cheng, Tianfu Wu, Christopher Healey

LogiGAN: Learning Logical Reasoning via Adversarial Pre-training

Xinyu Pi, Wanjun Zhong, Yan Gao, Nan Duan, Jian-Guang Lou

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation

Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel

Active Bayesian Causal Inference

Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf

Robust Learning against Relational Adversaries

Yizhen Wang, Mohannad Alhanahnah, Xiaozhu Meng, Ke Wang, Mihai Christodorescu

Human-AI Collaborative Bayesian Optimisation

Arun Kumar A V, Santu Rana, Alistair Shilton, Svetha Venkatesh

Ask4Help: Learning to Leverage an Expert for Embodied Tasks

Kunal Pratap Singh, Luca Weihs, Alvaro Herrasti, Jonghyun Choi, Aniruddha Kembhavi

On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting

Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman

Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery

Wiebke Günther, Urmi Ninad, Jonas Wahl, Jakob Runge

Inherently Explainable Reinforcement Learning in Natural Language

Xiangyu Peng, Mark Riedl, Prithviraj Ammanabrolu

Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer

Sen Lin, Li Yang, Deliang Fan, Junshan Zhang

Efficient Architecture Search for Diverse Tasks

Junhong Shen, Misha Khodak, Ameet Talwalkar

ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

Yihong Chen, Pushkar Mishra, Luca Franceschi, Pasquale Minervini, Pontus Lars Erik Saito Stenetorp

Normalizing Flows for Knockoff-free Controlled Feature Selection

Derek Hansen, Brian Manzo, Jeffrey Regier

Improved techniques for deterministic l2 robustness

Sahil Singla, Soheil Feizi

Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning

HYUNWOOK KANG, Taehwan Kwon, Jinkyoo Park, James R. Morrison

RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning

Marc Rigter, Bruno Lacerda, Nick Hawes

On the Importance of Gradient Norm in PAC-Bayesian Bounds

Itai Gat, Yossi Adi, Alex Schwing, Tamir Hazan

No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit

Rylan Schaeffer, Mikail Khona, Ila Fiete

Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation

Jinlong Li, Zequn Jie, Xu Wang, Xiaolin Wei, Lin Ma

Training Subset Selection for Weak Supervision

Hunter Lang, Aravindan Vijayaraghavan, David Sontag

Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres

Joshua Mitton, Simon Mekhail, Miles Padgett, Daniele Faccio, Marco Aversa

What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment

Nathan Kallus

k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension

Ziv Goldfeld, Kristjan Greenewald, Theshani Nuradha, Galen Reeves

Robust Generalized Method of Moments: A Finite Sample Viewpoint

Dhruv Rohatgi, Vasilis Syrgkanis

Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion

Haixu Ma, Donglin Zeng, Yufeng Liu

Cross-Linked Unified Embedding for cross-modality representation learning

Xinming Tu, Zhi-Jie Cao, xia chenrui, Sara Mostafavi, Ge Gao

AutoML Two-Sample Test

Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf

Sequential Information Design: Learning to Persuade in the Dark

Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò

Mining Multi-Label Samples from Single Positive Labels

Youngin Cho, Daejin Kim, MOHAMMAD AZAM KHAN, Jaegul Choo

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging

Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié

Efficiency Ordering of Stochastic Gradient Descent

Jie Hu, Vishwaraj Doshi, Do-Young Eun

Co-Modality Graph Contrastive Learning for Imbalanced Node Classification

Yiyue Qian, Chunhui Zhang, Yiming Zhang, Qianlong Wen, Yanfang Ye

Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning

Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality

Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash

The Hessian Screening Rule

Johan Larsson, Jonas Wallin

Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification

Jian Yang, Kai Zhu, Kecheng Zheng, Yang Cao

Better Best of Both Worlds Bounds for Bandits with Switching Costs

Idan Amir, Guy Azov, Tomer Koren, Roi Livni

OpenXAI: Towards a Transparent Evaluation of Model Explanations

Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson

Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors

Wenhao Zhang, Ying Nian Wu, Si Wu

PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning

Hanhan Zhou, Tian Lan, Vaneet Aggarwal

Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization

Caio Kalil Lauand, Sean Meyn

On Batch Teaching with Sample Complexity Bounded by VCD

Farnam Mansouri, Hans Simon, Adish Singla, Sandra Zilles

Iron: Private Inference on Transformers

Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu

Task Discovery: Finding the Tasks that Neural Networks Generalize on

Andrei Atanov, Andrei Filatov, Teresa Yeo, Ajay Sohmshetty, Amir Zamir

Identifiability of deep generative models without auxiliary information

Bohdan Kivva, Goutham Rajendran, Bryon Aragam, Pradeep K Ravikumar, Pradeep K. Ravikumar

Wasserstein Iterative Networks for Barycenter Estimation

Alexander Korotin, Vage Egiazarian, Lingxiao Li, Evgeny Burnaev

Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?

Patrick Dendorfer, Vladimir Yugay, Aljosa Osep, Laura Leal-Taixé

Linear Label Ranking with Bounded Noise

Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos

Collaborative Learning by Detecting Collaboration Partners

Shu Ding, Wei Wang

Using Embeddings for Causal Estimation of Peer Influence in Social Networks

Irina Cristali, Victor Veitch

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum