NeurIPS 2023

3,540 papers

Distributed Inference and Fine-tuning of Large Language Models Over The Internet

Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers

Bridging Discrete and Backpropagation: Straight-Through and Beyond

Liyuan Liu, Chengyu Dong, Xiaodong Liu, Bin Yu, Jianfeng Gao

Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long

A Partially-Supervised Reinforcement Learning Framework for Visual Active Search

Anindya Sarkar, Nathan Jacobs, Yevgeniy Vorobeychik

Deep Recurrent Optimal Stopping

Niranjan Damera Venkata, Chiranjib Bhattacharyya

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback

Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha

Point Cloud Completion with Pretrained Text-to-Image Diffusion Models

Yoni Kasten, Ohad Rahamim, Gal Chechik

DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning

Wenxuan Bao, Francesco Pittaluga, Vijay Kumar B G, Vincent Bindschaedler

Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach

Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli, Marcello Restelli

Counterfactually Fair Representation

Zhiqun Zuo, Mahdi Khalili, Xueru Zhang

f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences

Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang

Sheaf Hypergraph Networks

Iulia Duta, Giulia Cassarà, Fabrizio Silvestri, Pietro Lió

Inner-Outer Aware Reconstruction Model for Monocular 3D Scene Reconstruction

Yu-Kun Qiu, Guo-Hao Xu, Wei-Shi Zheng

FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation

Xinyu Sun, Peihao Chen, Jugang Fan, Jian Chen, Thomas Li

RanPAC: Random Projections and Pre-trained Models for Continual Learning

Mark D. McDonnell, Dong Gong, Amin Parvaneh, Ehsan Abbasnejad, Anton van den Hengel

MAG-GNN: Reinforcement Learning Boosted Graph Neural Network

Lecheng Kong, Jiarui Feng, Hao Liu, Dacheng Tao, Yixin Chen

On the Learnability of Multilabel Ranking

Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari

Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation

Susung Hong, Donghoon Ahn, Seungryong Kim

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets

Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio

What can a Single Attention Layer Learn? A Study Through the Random Features Lens

Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei

Learning to Reason and Memorize with Self-Notes

Jack Lanchantin, Shubham Toshniwal, Jason Weston, arthur szlam, Sainbayar Sukhbaatar

High-dimensional Asymptotics of Denoising Autoencoders

Hugo Cui, Lenka Zdeborová

What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding

Nicolas Keriven, Samuel Vaiter

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths

2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression

Alexander Tyurin, Peter Richtarik

Active Reasoning in an Open-World Environment

Manjie Xu, Guangyuan Jiang, Wei Liang, Chi Zhang, Yixin Zhu

MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues

Jia Jinrang, Zhenjia Li, Yifeng Shi

Low Tensor Rank Learning of Neural Dynamics

Arthur Pellegrino, N Alex Cayco Gajic, Angus Chadwick

Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift

Saurabh Garg, Amrith Setlur, Zachary Lipton, Sivaraman Balakrishnan, Virginia Smith

Revisiting the Minimalist Approach to Offline Reinforcement Learning

Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, Sergey Kolesnikov

Analysis of Variance of Multiple Causal Networks

Zhongli Jiang, Dabao Zhang

Towards Last-layer Retraining for Group Robustness with Fewer Annotations

Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar

SHAP-IQ: Unified Approximation of any-order Shapley Interactions

Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer

Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits

Dorian Baudry, Fabien Pesquerel, Rémy Degenne, Odalric-ambrym Maillard

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Zhongang Cai, Wanqi Yin, Ailing Zeng, CHEN WEI, Qingping SUN

Are GATs Out of Balance?

Nimrah Mustafa, Aleksandar Bojchevski, Rebekka Burkholz

Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation

Shengpu Tang, Jenna Wiens

Fast Trainable Projection for Robust Fine-tuning

Junjiao Tian, Yen-Cheng Liu, James S Smith, Zsolt Kira

SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation

Mengcheng Lan, Xinjiang Wang, Yiping Ke, Jiaxing Xu, Litong Feng

Localized Symbolic Knowledge Distillation for Visual Commonsense Models

Jae Sung Park, Jack Hessel, Khyathi Chandu, Paul Pu Liang, Ximing Lu

Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding

George Ma, Yifei Wang, Yisen Wang

Contextual Bandits and Imitation Learning with Preference-Based Active Queries

Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu

End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes

Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou Ammar

Solving a Class of Non-Convex Minimax Optimization in Federated Learning

Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang, Heng Huang

Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks

Woojin Cho, Kookjin Lee, Donsub Rim, Noseong Park

MIM4DD: Mutual Information Maximization for Dataset Distillation

Yuzhang Shang, Zhihang Yuan, Yan Yan

Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space

Xiran Fan, Chun-Hao Yang, Baba Vemuri

Federated Learning via Meta-Variational Dropout

Insu Jeon, Minui Hong, Junhyeog Yun, Gunhee Kim

Loss Decoupling for Task-Agnostic Continual Learning

Yan-Shuo Liang, Wu-jun Li

Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models

Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao

Non-adversarial training of Neural SDEs with signature kernel scores

Zacharia Issa, Blanka Horvath, Maud Lemercier, Cristopher Salvi

Contrastive Training of Complex-Valued Autoencoders for Object Discovery

Aleksandar Stanić, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber

Kernelized Cumulants: Beyond Kernel Mean Embeddings

Patric Bonnier, Harald Oberhauser, Zoltan Szabo

What Knowledge Gets Distilled in Knowledge Distillation?

Utkarsh Ojha, Yuheng Li, Anirudh Sundara Rajan, Yingyu Liang, Yong Jae Lee

How hard are computer vision datasets? Calibrating dataset difficulty to viewing time

David Mayo, Jesse Cummings, Xinyu Lin, Dan Gutfreund, Boris Katz

From ViT Features to Training-free Video Object Segmentation via Streaming-data Mixture Models

Roy Uziel, Or Dinari, Oren Freifeld

Bayesian target optimisation for high-precision holographic optogenetics

Marcus Triplett, Marta Gajowa, Hillel Adesnik, Liam Paninski

Sparsity-Preserving Differentially Private Training of Large Embedding Models

Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

RRHF: Rank Responses to Align Language Models with Human Feedback

Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Wei Wang, Songfang Huang

Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts

Ruth Dannenfelser, Jeffrey Zhong, Ran Zhang, Vicky Yao

Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily

Langzhang Liang, Xiangjing Hu, Zenglin Xu, Zixing Song, Irwin King

Robust Lipschitz Bandits to Adversarial Corruptions

Yue Kang, Cho-jui Hsieh, Thomas Chun Man Lee

Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training

Tiansheng Huang, Sihao Hu, Ka-Ho Chow, Fatih Ilhan, Selim Tekin

Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks

Eshaan Nichani, Alex Damian, Jason Lee

Reading Relevant Feature from Global Representation Memory for Visual Object Tracking

Xinyu Zhou, Pinxue Guo, Lingyi Hong, Jinglun Li, Wei Zhang

Improving Adversarial Robustness via Information Bottleneck Distillation

Huafeng Kuang, Hong Liu, Yongjian Wu, Shin'ichi Satoh, Rongrong Ji

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Zhenrui Li, Guojun Peng

Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection

Chengsen Wang, Zirui Zhuang, Qi Qi, Jingyu Wang, Xingyu Wang

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings

Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion

ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction

Jia Guo, Shuai Lu, Lize Jia, Weihang Zhang, Huiqi Li

Block-Coordinate Methods and Restarting for Solving Extensive-Form Games

Darshan Chakrabarti, Jelena Diakonikolas, Christian Kroer

Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks

Wenhan Yang, Jingdong Gao, Baharan Mirzasoleiman

IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI

Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li

On Measuring Fairness in Generative Models

Christopher Teo, Milad Abdollahzadeh, Ngai-Man (Man) Cheung

Evaluating and Inducing Personality in Pre-trained Language Models

Guangyuan Jiang, Manjie Xu, Song-chun Zhu, Wenjuan Han, Chi Zhang

VoxDet: Voxel Learning for Novel Instance Detection

Bowen Li, Jiashun Wang, Yaoyu Hu, Chen Wang, Sebastian Scherer

No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand

Mengzi Amy Guo, Donghao Ying, Javad Lavaei, Zuo-Jun Shen

Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization

Daesung Kim, Hye Won Chung

DreamHuman: Animatable 3D Avatars from Text

Nikos Kolotouros, Thiemo Alldieck, Andrei Zanfir, Eduard Bazavan, Mihai Fieraru

Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization

Yueming LYU

Online learning of long-range dependencies

Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento

Convolution Monge Mapping Normalization for learning on sleep data

Théo Gnassounou, Rémi Flamary, Alexandre Gramfort

How to Data in Datathons

Carlos Mougan, Richard Plant, Clare Teng, Marya Bazzi, Alvaro Cabrejas Egea

Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians

Rainer Engelken

Bandit Social Learning under Myopic Behavior

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins

CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation

Sihan Xu, Ziqiao Ma, Yidong Huang, Honglak Lee, Joyce Chai

Optimization of Inter-group criteria for clustering with minimum size constraints

Eduardo Laber, Lucas Murtinho

Active Vision Reinforcement Learning under Limited Visual Observability

Jinghuan Shang, Michael S. Ryoo, Michael S Ryoo

Recommender Systems with Generative Retrieval

Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu

Stable and low-precision training for large-scale vision-language models

Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi

AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation

Chaofan Ma, Yang Yuhuan, Chen Ju, Fei Zhang, Ya Zhang

Graph Denoising Diffusion for Inverse Protein Folding

Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Lió, Yuguang Wang

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

Xiaoxiao Sun, Nidham Gazagnadou, Vivek Sharma, Lingjuan Lyu, Hongdong Li

On Single-Index Models beyond Gaussian Data

Aaron Zweig, Loucas Pillaud-Vivien, Joan Bruna

LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings

Ningyi Liao, Siqiang Luo, Xiang Li, Jieming SHI

Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context

Julian Tanke, Oh-Hun Kwon, Felix B Mueller, Andreas Doering, Jürgen Gall

Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale

Zhiwei Hao, Jianyuan Guo, Kai Han, Han Hu, Chang Xu

Tester-Learners for Halfspaces: Universal Algorithms

Aravind Gollakota, Adam Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning

Ping Guo, Xiangpeng Wei, Yue Hu, Baosong Yang, Dayiheng Liu

QLoRA: Efficient Finetuning of Quantized LLMs

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer