NeurIPS 2023

3,540 papers

Scalarization for Multi-Task and Multi-Domain Learning at Scale

Amelie Royer, Tijmen Blankevoort, Babak Ehteshami Bejnordi

Robust Learning for Smoothed Online Convex Optimization with Feedback Delay

Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren

Learning DAGs from Data with Few Root Causes

Panagiotis Misiakos, Chris Wendler, Markus Püschel

Flow Matching for Scalable Simulation-Based Inference

Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen Green, Bernhard Schölkopf

Full-Atom Protein Pocket Design via Iterative Refinement

ZAIXI ZHANG, Zepu Lu, Hao Zhongkai, Marinka Zitnik, Qi Liu

Best Arm Identification with Fixed Budget: A Large Deviation Perspective

Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization

Xilie Xu, Jingfeng ZHANG, Feng Liu, Masashi Sugiyama, Mohan S Kankanhalli

Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization

Haggai Agmon, Yoram Burak

Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms

Qining Zhang, Lei Ying

Structural Pruning for Diffusion Models

Gongfan Fang, Xinyin Ma, Xinchao Wang

Likelihood-Based Diffusion Language Models

Ishaan Gulrajani, Tatsunori B Hashimoto

Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation

Xin Yuan, Pedro Savarese, Michael Maire

Debiased and Denoised Entity Recognition from Distant Supervision

Haobo Wang, Yiwen Dong, Ruixuan Xiao, Fei Huang, Gang Chen

An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond

Marc Jourdan, Rémy Degenne, Emilie Kaufmann

Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model

Alexander Tyurin, Peter Richtarik

Strategic Data Sharing between Competitors

Nikita Tsoy, Nikola Konstantinov

FLAIR : a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery

Anatol Garioud, Nicolas Gonthier, Loic Landrieu, Apolline De Wit, Marion Valette

Expressive Sign Equivariant Networks for Spectral Geometric Learning

Derek Lim, Joshua W. Robinson, Stefanie Jegelka, Haggai Maron

Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design

Ibrahim M Alabdulmohsin, Xiaohua Zhai, Alexander Kolesnikov, Lucas Beyer

On the Convergence of No-Regret Learning Dynamics in Time-Varying Games

Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm

Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation

Hongcheng Wang, Andy Guan Hong Chen, Xiaoqi Li, Mingdong Wu, Hao Dong

Towards Automated Circuit Discovery for Mechanistic Interpretability

Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, Adrià Garriga-Alonso

PRODIGY: Enabling In-context Learning Over Graphs

Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng

Expert load matters: operating networks at high accuracy and low manual effort

Sara Sangalli, Ertunc Erdil, Ender Konukoglu

Fast Optimal Locally Private Mean Estimation via Random Projections

Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar

Neural (Tangent Kernel) Collapse

Mariia Seleznova, Dana Weitzner, Raja Giryes, Gitta Kutyniok, Hung-Hsu Chou

Diffusion Self-Guidance for Controllable Image Generation

Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, Aleksander Holynski

Koopman Kernel Regression

Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier

Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes

Cai Zhou, Xiyuan Wang, Muhan Zhang

Large-Scale Distributed Learning via Private On-Device LSH

Tahseen Rabbani, Marco Bornstein, Furong Huang

Simple and Asymmetric Graph Contrastive Learning without Augmentations

Teng Xiao, Huaisheng Zhu, Zhengyu Chen, Suhang Wang

Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation

Kun Huang, Xin Guo, Meng Wang

Rank-DETR for High Quality Object Detection

Yifan Pu, WEICONG LIANG, Yiduo Hao, YUHUI YUAN, Yukang Yang

Any-to-Any Generation via Composable Diffusion

Zineng Tang, Ziyi Yang, Chenguang Zhu, Michael Zeng, Mohit Bansal

Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework

Ziyi Huang, Henry Lam, Amirhossein Meisami, Haofeng Zhang

PlanE: Representation Learning over Planar Graphs

Radoslav Dimitrov, Zeyang Zhao, Ralph Abboud, Ismail Ceylan

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution

Ying Wang, Tim G. J. Rudner, Andrew G Wilson

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs

Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang

Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference

Licong Lin, Mufang Ying, Suvrojit Ghosh, Koulik Khamaru, Cun-hui Zhang

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning

Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry P Vetrov, Ekaterina Lobacheva

ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li

Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models

Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Yunpeng Chen

DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models

XiMing Xing, Chuang Wang, Haitao Zhou, Jing Zhang, Qian Yu

CaMP: Causal Multi-policy Planning for Interactive Navigation in Multi-room Scenes

Xiaohan Wang, Yuehu Liu, Xinhang Song, Beibei Wang, Shuqiang Jiang

Adaptive Privacy Composition for Accuracy-first Mechanisms

Ryan M Rogers, Gennady Samorodnitsk, Steven Z. Wu, Aaditya Ramdas

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations

Hammaad Adam, Fan Yin, Huibin Hu, Neil Tenenholtz, Lorin Crawford

Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective

Yifei Wang, Liangchen Li, Jiansheng Yang, Zhouchen Lin, Yisen Wang

Parameter-efficient Tuning of Large-scale Multimodal Foundation Model

Haixin Wang, Xinlong Yang, Jianlong Chang, Dian Jin, Jinan Sun

An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint

Mengzhao Wang, Lingwei Lv, Xiaoliang Xu, Qiang Yue, Jiongkang Ni

Deep Equilibrium Based Neural Operators for Steady-State PDEs

Tanya Marwah, Ashwini Pokle, J. Zico Kolter, Zachary Lipton, Jianfeng Lu

Kernel-Based Tests for Likelihood-Free Hypothesis Testing

Patrik Robert Gerber, Tianze Jiang, Yury Polyanskiy, Rui Sun

One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning

Zichang Liu, Zhaozhuo Xu, Benjamin Coleman, Anshumali Shrivastava

Physics-Driven ML-Based Modelling for Correcting Inverse Estimation

ruiyuan kang, Tingting Mu, Panagiotis Liatsis, Dimitrios Kyritsis

Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Xinyi Wang, Wanrong Zhu, Michael Saxon, Mark Steyvers, William Yang Wang

Distributional Pareto-Optimal Multi-Objective Reinforcement Learning

Xin-Qiang Cai, Pushi Zhang, Li Zhao, Jiang Bian, Masashi Sugiyama

Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion

Junliang Li, Yang Yajun, Qinghua Hu, Xin Wang, Hong Gao

Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction

Taiji Suzuki, Denny Wu, Atsushi Nitanda

Online Constrained Meta-Learning: Provable Guarantees for Generalization

Siyuan Xu, Minghui Zhu

DynaDojo: An Extensible Platform for Benchmarking Scaling in Dynamical System Identification

Logan M Bhamidipaty, Tommy Bruzzese, Caryn Tran, Rami Ratl Mrad, Maxinder S. Kanwal

Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs

Peng Jin, Yang Wu, Yanbo Fan, Zhongqian Sun, Wei Yang

Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness

Gang Li, Wei Tong, Tianbao Yang

ELDEN: Exploration via Local Dependencies

Zizhao Wang, Jiaheng Hu, Peter Stone, Roberto Martín-Martín

Optimal Exploration for Model-Based RL in Nonlinear Systems

Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson

Rethinking the Role of Token Retrieval in Multi-Vector Retrieval

Jinhyuk Lee, Zhuyun Dai, Sai Meher Karthik Duddu, Tao Lei, Iftekhar Naim

Goal-conditioned Offline Planning from Curious Exploration

Marco Bagatella, Georg Martius

Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice

Aiwen Xu, Yuchen Hou, Cristopher Niell, Michael Beyeler

A Metadata-Driven Approach to Understand Graph Neural Networks

Ting Wei Li, Qiaozhu Mei, Jiaqi Ma

ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training

Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà

Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing

Jung Yeon Park, Lawson Wong, Robin Walters

Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications

Xinyu Ma, Xu Chu, Yasha Wang, Yang Lin, Junfeng Zhao

Lightweight Vision Transformer with Bidirectional Interaction

Qihang Fan, Huaibo Huang, Xiaoqiang Zhou, Ran He

Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization

Jinxin Liu, Hongyin Zhang, Zifeng Zhuang, Yachen Kang, Donglin Wang

Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning

Baohao Liao, Shaomu Tan, Christof Monz

Replicable Reinforcement Learning

Eric Eaton, Marcel Hussing, Michael J. Kearns, Jessica Sorrell

Memory Efficient Optimizers with 4-bit States

Bingrui Li, Jianfei Chen, Jun Zhu

Data Portraits: Recording Foundation Model Training Data

Marc Marone, Benjamin Van Durme

Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks

Changhyeon Lee, Seulki Lee

Alleviating the Semantic Gap for Generalized fMRI-to-Image Reconstruction

Tao Fang, Qian Zheng, Gang Pan

RADAR: Robust AI-Text Detection via Adversarial Learning

Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho

Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration

Qi-Wei Wang, Da-Wei Zhou, De-Chuan Zhan, Han-Jia Ye, Yikai Zhang

A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm

Haizhou Shi, Hao Wang

When are ensembles really effective?

Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney

Persuading Farsighted Receivers in MDPs: the Power of Honesty

Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Mirco Mutti

Jigsaw: Learning to Assemble Multiple Fractured Objects

Jiaxin Lu, Yifan Sun, Qixing Huang

Easy Learning from Label Proportions

Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andres Munoz Medina

Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought

Yuki Wang, Gonzalo Gonzalez-Pumariega, Yash Sharma, Sanjiban Choudhury

Learning Reliable Logical Rules with SATNet

Zhaoyu Li, Jinpei Guo, Yuhe Jiang, Xujie Si

FedL2P: Federated Learning to Personalize

Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy Hospedales

Functional Renyi Differential Privacy for Generative Modeling

Dihong Jiang, Sun Sun, Yaoliang Yu

Quasi-Monte Carlo Graph Random Features

Isaac Reid, Krzysztof M Choromanski, Adrian Weller

Safety Verification of Decision-Tree Policies in Continuous Time

Christian Schilling, Anna Lukina, Emir Demirović, Kim Larsen

AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders

Paribesh Regmi, Rui Li

Efficient Low-rank Backpropagation for Vision Transformer Adaptation

Yuedong Yang, Hung-Yueh Chiang, Guihong Li, Diana Marculescu, Radu Marculescu

On skip connections and normalisation layers in deep optimisation

Lachlan MacDonald, Jack Valmadre, Hemanth Saratchandran, Simon Lucey

Agnostically Learning Single-Index Models using Omnipredictors

Aravind Gollakota, Parikshit Gopalan, Adam Klivans, Konstantinos Stavropoulos

Gaussian Differential Privacy on Riemannian Manifolds

Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong

Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity

Dong Kyum Kim, Jea Kwon, Meeyoung Cha, C. Lee

Human-Aligned Calibration for AI-Assisted Decision Making

Nina Corvelo Benz, Manuel Rodriguez

Supply-Side Equilibria in Recommender Systems

Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt

Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models

Waïss Azizian, Franck Iutzeler, Jérôme Malick