NeurIPS 2023

3,540 papers

MADLAD-400: A Multilingual And Document-Level Large Audited Dataset

Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Derrick Xin

CamoPatch: An Evolutionary Strategy for Generating Camoflauged Adversarial Patches

Phoenix Williams, Ke Li

Selective Sampling and Imitation Learning via Online Regression

Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu

Efficient Diffusion Policies For Offline Reinforcement Learning

Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, Shuicheng Yan

Machine learning detects terminal singularities

Tom Coates, Alexander Kasprzyk, Sara Veneziale

State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding

Devleena Das, Sonia Chernova, Been Kim

SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction

Kushin Mukherjee, Holly Huey, Xuanchen Lu, Yael Vinker, Rio Aguina-Kang

EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason Fries, Nigam Shah

A General Framework for Robust G-Invariance in G-Equivariant Networks

Sophia Sanborn, Nina Miolane

Classical Simulation of Quantum Circuits: Parallel Environments and Benchmark

Xiao-Yang Liu, Zeliang Zhang

HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding

Hao Zheng, Regina Lee, Yuqian Lu

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen, Daniel Bear, Chuang Gan

Autodecoding Latent 3D Diffusion Models

Evangelos Ntavelis, Aliaksandr Siarohin, Kyle Olszewski, Chaoyang Wang, Luc V Gool

Secure Out-of-Distribution Task Generalization with Energy-Based Models

Shengzhuang Chen, Long-Kai Huang, Jonathan Richard Schwarz, Yilun Du, Ying Wei

Exploiting hidden structures in non-convex games for convergence to Nash equilibrium

Iosif Sakos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation

David Brandfonbrener, Ofir Nachum, Joan Bruna

Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition

Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab

Optimizing Prompts for Text-to-Image Generation

Yaru Hao, Zewen Chi, Li Dong, Furu Wei

Order Matters in the Presence of Dataset Imbalance for Multilingual Learning

Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg

Random Cuts are Optimal for Explainable k-Medians

Konstantin Makarychev, Liren Shan

StyleDrop: Text-to-Image Synthesis of Any Style

Kihyuk Sohn, Lu Jiang, Jarred Barber, Kimin Lee, Nataniel Ruiz

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage

Jose Blanchet, Miao Lu, Tong Zhang, Han Zhong

Near-Linear Time Algorithm for the Chamfer Distance

Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten

Convex-Concave Zero-Sum Markov Stackelberg Games

Denizalp Goktas, Arjun Prakash, Amy Greenwald

Efficient Model-Free Exploration in Low-Rank MDPs

Zak Mhammedi, Adam Block, Dylan J Foster, Alexander Rakhlin

DiViNeT: 3D Reconstruction from Disparate Views using Neural Template Regularization

Aditya Vora, Akshay Gadi Patil, Hao Zhang

ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP

Lu Yan, Zhuo Zhang, Guanhong Tao, Kaiyuan Zhang, Xuan Chen

Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models

Guillermo Ortiz-Jimenez, Alessandro Favero, Pascal Frossard

Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs

Dingmin Wang, Yeyuan Chen

Scalable Fair Influence Maximization

Xiaobin Rui, Zhixiao Wang, Jiayu Zhao, Lichao Sun, Wei Chen

A new perspective on building efficient and expressive 3D equivariant graph neural networks

weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang

PAC Learning Linear Thresholds from Label Proportions

Anand Brahmbhatt, Rishi Saket, Aravindan Raghuveer

Particle-based Variational Inference with Generalized Wasserstein Gradient Flow

Ziheng Cheng, Shiyue Zhang, Longlin Yu, Cheng Zhang

Feature Learning for Interpretable, Performant Decision Trees

Jack Good, Torin Kovach, Kyle Miller, Artur Dubrawski

Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity

Weichao Mao, Haoran Qiu, Chen Wang, Hubertus Franke, Zbigniew Kalbarczyk

Differentiable sorting for censored time-to-event data.

Andre Vauvelle, Benjamin Wild, Roland Eils, Spiros Denaxas

Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models

Deepak Narayanan, Keshav Santhanam, Peter Henderson, Rishi Bommasani, Tony Lee

Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels

Jian Chen, Ruiyi Zhang, Tong Yu, Rohan Sharma, Zhiqiang Xu

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees

Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad Harikandeh, Nicolas Le Roux

Structured Neural Networks for Density Estimation and Causal Inference

Asic Chen, Ruian (Ian) Shi, Xiang Gao, Ricardo Baptista, Rahul G Krishnan

On the Connection between Pre-training Data Diversity and Fine-tuning Robustness

Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ali Farhadi, Ludwig Schmidt

A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks

Sara Babakniya, Zalan Fabian, Chaoyang He, Mahdi Soltanolkotabi, Salman Avestimehr

RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing

Antoine Scardigli, Lukas Cavigelli, Lorenz K. Müller

Spontaneous symmetry breaking in generative diffusion models

Gabriel Raya, Luca Ambrogioni

IBA: Towards Irreversible Backdoor Attacks in Federated Learning

Thuy Dung Nguyen, Tuan A Nguyen, Anh Tran, Khoa D Doan, Kok-Seng Wong

Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation

Wenhao Ding, Laixi Shi, Yuejie Chi, Ding Zhao

Provable convergence guarantees for black-box variational inference

Justin Domke, Robert Gower, Guillaume Garrigos

ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram

Jungwoo Oh, Gyubok Lee, Seongsu Bae, Joon-myoung Kwon, Edward Choi

On the Overlooked Structure of Stochastic Gradients

Zeke Xie, Qian-Yuan Tang, Mingming Sun, Ping Li

Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations

Piotr Indyk, Haike Xu

Unleashing the Power of Randomization in Auditing Differentially Private ML

Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea

Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs

Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Alejandro Ribeiro

GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition

Vikram V. Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron Adcock, Laurens van der Maaten

Feature Selection in the Contrastive Analysis Setting

Ethan Weinberger, Ian Covert, Su-in Lee

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

Shengchao Liu, weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng

Robust low-rank training via approximate orthonormal constraints

Dayana Savostianova, Emanuele Zangrando, Gianluca Ceruti, Francesco Tudisco

LEACE: Perfect linear concept erasure in closed form

Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff

Gacs-Korner Common Information Variational Autoencoder

Michael Kleinman, Alessandro Achille, Stefano Soatto, Jonathan Kao

NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function

Qing Li, Huifang Feng, Kanle Shi, Yue Gao, Yi Fang

Experimental Designs for Heteroskedastic Variance

Justin Weltz, Tanner Fiez, Alexander Volfovsky, Eric Laber, Blake Mason

MomentDiff: Generative Video Moment Retrieval from Random to Real

Pandeng Li, Chen-Wei Xie, Hongtao Xie, Liming Zhao, Lei Zhang

Strong and Precise Modulation of Human Percepts via Robustified ANNs

Guy Gaziv, Michael D. Lee, James J DiCarlo

AMDP: An Adaptive Detection Procedure for False Discovery Rate Control in High-Dimensional Mediation Analysis

Jiarong Ding, Xuehu ZHU

FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression

Youguang Chen, George Biros

Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods

Tobit Klug, Dogukan Atik, Reinhard Heckel

Kernel Quadrature with Randomly Pivoted Cholesky

Ethan Epperly, Elvira Moreno

Temporal Continual Learning with Prior Compensation for Human Motion Prediction

Jianwei Tang, Jiangxin Sun, Xiaotong Lin, lifang zhang, Wei-Shi Zheng

Deep Contract Design via Discontinuous Networks

Tonghan Wang, Paul Duetting, Dmitry Ivanov, Inbal Talgam-Cohen, David C. Parkes

ODE-based Recurrent Model-free Reinforcement Learning for POMDPs

Xuanle Zhao, Duzhen Zhang, Han Liyuan, Tielin Zhang, Bo Xu

Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds

Jihun Yun, Eunho Yang

Slot-guided Volumetric Object Radiance Fields

DI QI, Tong Yang, Xiangyu Zhang

Self-supervised video pretraining yields robust and more human-aligned visual representations

Nikhil Parthasarathy, S. M. Ali Eslami, Joao Carreira, Olivier Henaff

Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms

Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Lipton

Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees

Veronica Alvarez, Santiago Mazuelas, Jose A. Lozano

TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models

Jiaqi Xue, Mengxin Zheng, Ting Hua, Yilin Shen, Yepeng Liu

Revealing the unseen: Benchmarking video action recognition under occlusion

Shresth Grover, Vibhav Vineet, Yogesh Rawat

Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks

Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta

ZipLM: Inference-Aware Structured Pruning of Language Models

Eldar Kurtic, Elias Frantar, Dan Alistarh

Derandomized novelty detection with FDR control via conformal e-values

Meshi Bashari, Amir Epstein, Yaniv Romano, Matteo Sesia

Neural Priming for Sample-Efficient Adaptation

Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi

When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline

Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue, Yang Luo, Xin Jiang

Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation

Diederik Kingma, Ruiqi Gao

Post Hoc Explanations of Language Models Can Improve Language Models

Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh

Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments

Thanh-Dat Truong, Hoang-Quan Nguyen, Bhiksha Raj, Khoa Luu

Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning

Siyuan Sun, Hongyang Gao

Offline RL with Discrete Proxy Representations for Generalizability in POMDPs

Pengjie Gu, Xinyu Cai, Dong Xing, Xinrun Wang, Mengchen Zhao

Volume Feature Rendering for Fast Neural Radiance Field Reconstruction

Kang Han, Wei Xiang, Lu Yu

Learning Rate Free Sampling in Constrained Domains

Louis Sharrock, Lester W. Mackey, Christopher Nemeth

Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks

Yaoyu Zhu, Wei Fang, Xiaodong Xie, Tiejun Huang, Zhaofei Yu

BayesTune: Bayesian Sparse Deep Model Fine-tuning

Minyoung Kim, Timothy Hospedales

Knowledge Diffusion for Distillation

Tao Huang, yuan zhang, Mingkai Zheng, Shan You, Fei Wang

Does a sparse ReLU network training problem always admit an optimum ?

TUNG LE, Remi Gribonval, Elisa Riccietti

SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models

Xiaosong Ma, Jie Zhang, Song Guo, Wenchao Xu

SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning

Dohyeok Lee, Seungyub Han, Taehyun Cho, Jungwoo Lee

Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers

Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurelien Lucchi

Meta-in-context learning in large language models

Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matt Botvinick, Jane Wang

DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, James Yu

AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web

Michael Schlichtkrull, Zhijiang Guo, Andreas Vlachos

End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

Alexander Shmakov, Kevin Greif, Michael Fenton, Aishik Ghosh, Pierre Baldi