NeurIPS 2022

2,834 papers

Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes

Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal, Stephen Bach

Isometric 3D Adversarial Examples in the Physical World

yibo miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao

Online Frank-Wolfe with Arbitrary Delays

Yuanyu Wan, Wei-Wei Tu, Lijun Zhang

Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning

Milad Sefidgaran, Romain Chor, Abdellatif Zaidi

Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models

Yihan Zhang, Nir Weinberger

Redundant representations help generalization in wide neural networks

Diego Doimo, Aldo Glielmo, Sebastian Goldt, Alessandro Laio

Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving

Xiwen Liang, Yangxin Wu, Jianhua Han, Hang Xu, Chunjing XU

A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training

Randall Balestriero, Ishan Misra, Yann LeCun

A Geometric Perspective on Variational Autoencoders

Clément Chadebec, Stéphanie ALLASSONNIERE

Benign Underfitting of Stochastic Gradient Descent

Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman

Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks

Tao Sun, Dongsheng Li, Bao Wang

WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting

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

Surprising Instabilities in Training Deep Networks and a Theoretical Analysis

Yuxin Sun, DONG LAO, Ganesh Sundaramoorthi, Anthony Yezzi

Maximum Class Separation as Inductive Bias in One Matrix

Tejaswi Kasarla, Gertjan Burghouts, Max van Spengler, Elise van der Pol, Rita Cucchiara

Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection

Abir De, Soumen Chakrabarti

Beyond neural scaling laws: beating power law scaling via data pruning

Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari Morcos

Understanding Deep Contrastive Learning via Coordinate-wise Optimization

Yuandong Tian

Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers

Ganchao Wei, Ian H Stevenson, Xiao-Jing Wang, Xiaojing Wang

RISE: Robust Individualized Decision Learning with Sensitive Variables

Xiaoqing Tan, Zhengling Qi, Christopher Seymour, Lu Tang

Improving Variational Autoencoders with Density Gap-based Regularization

Jianfei Zhang, Jun Bai, Chenghua Lin, Yanmeng Wang, Wenge Rong

Concentration of Data Encoding in Parameterized Quantum Circuits

Guangxi Li, Ruilin Ye, Xuanqiang Zhao, Xin Wang

Learning to Follow Instructions in Text-Based Games

Mathieu Tuli, Andrew Li, Pashootan Vaezipoor, Toryn Klassen, Scott Sanner

Few-shot Image Generation via Adaptation-Aware Kernel Modulation

Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man (Man) Cheung

Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks

Yijing Liu, Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang

Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective

Bohang Zhang, Du Jiang, Di He, Liwei Wang

Measuring and Reducing Model Update Regression in Structured Prediction for NLP

Deng Cai, Elman Mansimov, Yi-An Lai, Yixuan Su, Lei Shu

InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation

Fei He, Haoyang Zhang, Naiyu Gao, Jian Jia, Yanhu Shan

Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss

Aleksandros Sobczyk, Mathieu Luisier

Theoretically Provable Spiking Neural Networks

Shao-Qun Zhang, Zhi-Hua Zhou

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Yue Tan, Guodong Long, Jie Ma, LU LIU, Tianyi Zhou

Towards Lightweight Black-Box Attack Against Deep Neural Networks

Chenghao Sun, Yonggang Zhang, Wan Chaoqun, qizhou wang, Ya Li

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings

Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M Alabdulmohsin, Eva Schnider

Large-Scale Differentiable Causal Discovery of Factor Graphs

Romain Lopez, Jan-Christian Huetter, Jonathan Pritchard, Aviv Regev

Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift

Christina Baek, YiDing Jiang, Aditi Raghunathan, J. Zico Kolter

Efficient identification of informative features in simulation-based inference

Jonas Beck, Michael Deistler, Yves Bernaerts, Philipp Berens, Jakob H. Macke

Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem

Raunak Kumar, Robert D. Kleinberg

Pareto Set Learning for Expensive Multi-Objective Optimization

Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang

Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees

Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao

Asymptotics of smoothed Wasserstein distances in the small noise regime

Yunzi Ding, Jonathan Niles-Weed

A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models

Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li, Peng Fu

Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model

Su Jia, Andrew Li, R Ravi

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch

Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein

Fairness without Demographics through Knowledge Distillation

Junyi Chai, Taeuk Jang, Xiaoqian Wang

Deconfounded Representation Similarity for Comparison of Neural Networks

Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski

A Unified Analysis of Federated Learning with Arbitrary Client Participation

Shiqiang Wang, Mingyue Ji

An α-No-Regret Algorithm For Graphical Bilinear Bandits

Geovani Rizk, Igor Colin, Albert Thomas, Rida Laraki, Yann Chevaleyre

Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity

Andrew Cullen, Paul Montague, Shijie Liu, Sarah Erfani, Benjamin I. Rubinstein

Trustworthy Monte Carlo

Juha Harviainen, Mikko Koivisto, Petteri Kaski

ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography

Ahmed M. Alaa, Anthony Philippakis, David Sontag

Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training

Geng Yuan, Yanyu Li, Sheng Li, Zhenglun Kong, Sergey Tulyakov

Efficient learning of nonlinear prediction models with time-series privileged information

Bastian Jung, Fredrik D Johansson

Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models

Fan LIU, Hao Liu, Wenzhao Jiang

Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization

Yuri Kinoshita, Taiji Suzuki

Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning

Guy Tennenholtz, Shie Mannor

AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies

Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu

Towards Theoretically Inspired Neural Initialization Optimization

Yibo Yang, Hong Wang, Haobo Yuan, Zhouchen Lin

Memory safe computations with XLA compiler

Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk

Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression

Xinmeng Huang, Yiming Chen, Wotao Yin, Kun Yuan

Reconstruction on Trees and Low-Degree Polynomials

Frederic Koehler, Elchanan Mossel

Regularized Molecular Conformation Fields

Lihao Wang, Yi Zhou, Yiqun Wang, Xiaoqing Zheng, Xuanjing Huang

Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks

Mansheej Paul, Brett Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite

LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness

Xiaojun Xu, Linyi Li, Bo Li

Algorithms that Approximate Data Removal: New Results and Limitations

Vinith Suriyakumar, Ashia C Wilson

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh

Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions

Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge

QC-StyleGAN - Quality Controllable Image Generation and Manipulation

Dat Viet Thanh Nguyen, Phong Tran The, Tan M. Dinh, Cuong Pham, Anh Tran

Convergent Representations of Computer Programs in Human and Artificial Neural Networks

Shashank Srikant, Ben Lipkin, Anna Ivanova, Evelina Fedorenko, Una-May O'Reilly

Quantized Training of Gradient Boosting Decision Trees

Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-yan Liu

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob Foerster, Michael Everett

Efficient Aggregated Kernel Tests using Incomplete U-statistics

Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton

SHINE: SubHypergraph Inductive Neural nEtwork

Yuan Luo

Unsupervised Learning under Latent Label Shift

Manley Roberts, Pranav Mani, Saurabh Garg, Zachary Lipton

Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition

Daeha Kim, Byung Cheol Song

Exploring through Random Curiosity with General Value Functions

Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

Rapid Model Architecture Adaption for Meta-Learning

Yiren Zhao, Xitong Gao, I Shumailov, Nicolo Fusi, Robert Mullins

Most Activation Functions Can Win the Lottery Without Excessive Depth

Rebekka Burkholz

Large-batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes

Zeyue Xue, Jianming Liang, Guanglu Song, Zhuofan Zong, Liang Chen

DaDA: Distortion-aware Domain Adaptation for Unsupervised Semantic Segmentation

Sujin Jang, Joohan Na, Dokwan Oh

Pre-activation Distributions Expose Backdoor Neurons

Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu

Private Estimation with Public Data

Alex Bie, Gautam Kamath, Vikrant Singhal

Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation

Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang, Congchong Nie, Jun Liu

A sharp NMF result with applications in network modeling

Jiashun Jin

Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits

Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, Dacheng Tao

When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang

On Elimination Strategies for Bandit Fixed-Confidence Identification

Andrea Tirinzoni, Rémy Degenne

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios

Mantas Mazeika, Eric Tang, Andy Zou, Steven Basart, Jun Shern Chan

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions

Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine

pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models

Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Jiliang Tang

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

Cristian Bodnar, Francesco Di Giovanni, Benjamin Chamberlain, Pietro Lió, Michael Bronstein

Learning to Branch with Tree MDPs

Lara Scavuzzo, Feng Chen, Didier Chetelat, Maxime Gasse, Andrea Lodi

On Scrambling Phenomena for Randomly Initialized Recurrent Networks

Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Stavroulakis

Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms

Jiechao Guan, Yong Liu, Zhiwu Lu

Measures of Information Reflect Memorization Patterns

Rachit Bansal, Danish Pruthi, Yonatan Belinkov

UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units

Igor Fedorov, Ramon Matas, Hokchhay Tann, Chuteng Zhou, Matthew Mattina

Unifying Voxel-based Representation with Transformer for 3D Object Detection

Yanwei Li, Yilun Chen, Xiaojuan Qi, Zeming Li, Jian Sun

Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels

Ming-Kun Xie, Jiahao Xiao, Sheng-jun Huang

Modeling the Machine Learning Multiverse

Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence

Learning Debiased Classifier with Biased Committee

Nayeong Kim, SEHYUN HWANG, Sungsoo Ahn, Jaesik Park, Suha Kwak

Truncated Matrix Power Iteration for Differentiable DAG Learning

Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad

DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning

Yuko Ishiwaka, Xiao Zeng, Shun Ogawa, Donovan Westwater, Tadayuki Tone