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ORCID37th NeurIPS 2023: New Orleans, LA, USA
- Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine:
Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. 2023 - Michael Bereket, Theofanis Karaletsos:
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder. - Lucy Xiaoyang Shi, Yunfan Jiang, Jake Grigsby, Linxi Fan, Yuke Zhu:
Cross-Episodic Curriculum for Transformer Agents. - Xiang Li, Chung-Ching Lin, Yinpeng Chen, Zicheng Liu, Jinglu Wang, Rita Singh, Bhiksha Raj:
PaintSeg: Painting Pixels for Training-free Segmentation. - Yiren Jian, Chongyang Gao, Soroush Vosoughi:
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training. - Yunzhang Zhu, Renxiong Liu:
Path following algorithms for 𝓁2-regularized M-estimation with approximation guarantee. - Yuhan Ding, Fukun Yin, Jiayuan Fan, Hui Li, Xin Chen, Wen Liu, Chongshan Lu, Gang Yu, Tao Chen:
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation. - Ruida Zhou, Tao Liu, Min Cheng, Dileep Kalathil, P. R. Kumar, Chao Tian:
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation. - Rui M. Castro, Fredrik Hellström, Tim van Erven:
Adaptive Selective Sampling for Online Prediction with Experts. - Mathias Lechner, Lianhao Yin, Tim Seyde, Tsun-Hsuan Johnson Wang, Wei Xiao, Ramin M. Hasani, Joshua Rountree, Daniela Rus:
Gigastep - One Billion Steps per Second Multi-agent Reinforcement Learning. - Xiaolei Ru, Xinya Zhang, Zijia Liu, Jack Murdoch Moore, Gang Yan:
Attentive Transfer Entropy to Exploit Transient Emergence of Coupling Effect. - Thad Starner, Sean Forbes, Matthew So, David Martin, Rohit Sridhar, Gururaj Deshpande, Sam S. Sepah, Sahir Shahryar, Khushi Bhardwaj, Tyler Kwok, Daksh Sehgal, Saad Hassan, Bill Neubauer, Sofia Anandi Vempala, Alec Tan, Jocelyn Heath, Unnathi Kumar, Priyanka Mosur, Tavenner Hall, Rajandeep Singh, Christopher Cui, Glenn Cameron, Sohier Dane, Garrett Tanzer:
PopSign ASL v1.0: An Isolated American Sign Language Dataset Collected via Smartphones. - Jan Schuchardt, Yan Scholten, Stephan Günnemann:
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. - Zhaoying Pan, Daniel Geng, Andrew Owens:
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow. - Xinrui Chen, Yizhi Wang, Renao Yan, Yiqing Liu, Tian Guan, Yonghong He:
TexQ: Zero-shot Network Quantization with Texture Feature Distribution Calibration. - Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans:
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. - Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. - Qiyao Huang, Yingyue Zhang, Zhihong Zhang, Edwin R. Hancock:
ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy. - Hui Guo, Boyu Wang, Grace Yi:
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model. - Mineui Hong, Minjae Kang, Songhwai Oh:
Diffused Task-Agnostic Milestone Planner. - Po-han Li, Sravan Kumar Ankireddy, Ruihan Philip Zhao, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari, Ufuk Topcu, Sandeep Chinchali, Hyeji Kim:
Task-aware Distributed Source Coding under Dynamic Bandwidth. - Sheikh Md Shakeel Hassan, Arthur Feeney, Akash Dhruv, Jihoon Kim, Youngjoon Suh, Jaiyoung Ryu, Yoonjin Won, Aparna Chandramowlishwaran:
BubbleML: A Multiphase Multiphysics Dataset and Benchmarks for Machine Learning. - Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic:
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation. - Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew J. Vowels, Jalal Etesami, Negar Kiyavash:
Causal Effect Identification in Uncertain Causal Networks. - Jia Gu, Caizhi Tang, Han Yan, Qing Cui, Longfei Li, Jun Zhou:
FAST: a Fused and Accurate Shrinkage Tree for Heterogeneous Treatment Effects Estimation. - Oleg Platonov, Denis Kuznedelev, Artem Babenko, Liudmila Prokhorenkova:
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond. - Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma:
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation. - Seunghyuk Cho, Juyong Lee, Dongwoo Kim:
Hyperbolic VAE via Latent Gaussian Distributions. - Kai Yan, Alexander G. Schwing, Yu-Xiong Wang:
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories. - Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David S. Doermann, Mingchen Gao:
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training. - David Skrill, Samuel Norman-Haignere:
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows. - Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier:
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? - Jangwon Kim, Hangyeol Kim, Jiwook Kang, Jongchan Baek, Soohee Han:
Belief Projection-Based Reinforcement Learning for Environments with Delayed Feedback. - Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart:
Batchnorm Allows Unsupervised Radial Attacks. - Yichao Cao, Qingfei Tang, Xiu Su, Song Chen, Shan You, Xiaobo Lu, Chang Xu:
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models. - Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee:
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. - Alexander G. Reisach, Myriam Tami, Christof Seiler, Antoine Chambaz, Sebastian Weichwald:
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models. - Anastasia Batsheva, Andrei Chertkov, Gleb V. Ryzhakov, Ivan V. Oseledets:
PROTES: Probabilistic Optimization with Tensor Sampling. - Junqi Gao, Biqing Qi, Yao Li, Zhichang Guo, Dong Li, Yuming Xing, Dazhi Zhang:
Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability. - Jeroen Berrevoets, Daniel Jarrett, Alex J. Chan, Mihaela van der Schaar:
AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems. - Ziniu Hu, Ahmet Iscen, Chen Sun, Kai-Wei Chang, Yizhou Sun, David Ross, Cordelia Schmid, Alireza Fathi:
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent. - Prasenjit Dey, Srujana Merugu, Sivaramakrishnan R. Kaveri:
Conformal Prediction Sets for Ordinal Classification. - Shivam Gupta, Jasper C. H. Lee, Eric Price, Paul Valiant:
Minimax-Optimal Location Estimation. - Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian:
Tight Bounds for Volumetric Spanners and Applications. - Mohammad Mahdi Kamani, Yuhang Yao, Hanjia Lyu, Zhongwei Cheng, Lin Chen, Liangju Li, Carlee Joe-Wong, Jiebo Luo:
Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. - Pingsheng Li, Jonathan Cornford, Arna Ghosh, Blake A. Richards:
Learning better with Dale's Law: A Spectral Perspective. - Valerii Likhosherstov, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller:
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel. - Khashayar Gatmiry, Zakaria Mhammedi:
Projection-Free Online Convex Optimization via Efficient Newton Iterations. - Yue Wu, Yewen Fan, Paul Pu Liang, Amos Azaria, Yuanzhi Li, Tom M. Mitchell:
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals. - Kaiyue Wen, Zhiyuan Li, Tengyu Ma:
Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization. - Nikhil Vyas, Alexander B. Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan, Cengiz Pehlevan:
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales. - Michele Garibbo, Maxime Robeyns, Laurence Aitchison:
Taylor TD-learning. - Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis:
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. - Nicholas Rittler, Kamalika Chaudhuri:
Agnostic Multi-Group Active Learning. - Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, Xiaofeng Zhu:
Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration. - Mingli Zhu, Shaokui Wei, Hongyuan Zha, Baoyuan Wu:
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features. - Dami Choi, Yonadav Shavit, David Kristjanson Duvenaud:
Tools for Verifying Neural Models' Training Data. - Yuchuan Tian, Hanting Chen, Tianyu Guo, Chao Xu, Yunhe Wang:
Towards Higher Ranks via Adversarial Weight Pruning. - Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama:
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. - Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Taesu Kim, Daehyun Ahn, Hyungjun Kim, Jae-Joon Kim:
Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis. - Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng:
Adversarial Model for Offline Reinforcement Learning. - Man Zhou, Naishan Zheng, Yuan Xu, Chun-Le Guo, Chongyi Li:
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function. - Andrew K. Lampinen, Stephanie C. Y. Chan, Ishita Dasgupta, Andrew J. Nam, Jane X. Wang:
Passive learning of active causal strategies in agents and language models. - Wenjing Yan, Xuanyu Cao:
Zero-Regret Performative Prediction Under Inequality Constraints. - Yichen Xie, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan:
Towards Free Data Selection with General-Purpose Models. - Junyi Li, Feihu Huang, Heng Huang:
Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems. - Jun-Yi Hang, Min-Ling Zhang:
Partial Multi-Label Learning with Probabilistic Graphical Disambiguation. - Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John P. Dickerson, Joseph Suarez:
Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks. - Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, Bharath Hariharan:
Emergent Correspondence from Image Diffusion. - Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu:
Robust Learning with Progressive Data Expansion Against Spurious Correlation. - Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran:
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. - Kruno Lehman, Alain Durmus, Umut Simsekli:
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. - Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang:
Uncovering Neural Scaling Laws in Molecular Representation Learning. - Cameron Smith, Yilun Du, Ayush Tewari, Vincent Sitzmann:
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow. - Milad Sefidgaran, Abdellatif Zaidi, Piotr Krasnowski:
Minimum Description Length and Generalization Guarantees for Representation Learning. - Robin San Roman, Yossi Adi, Antoine Deleforge, Romain Serizel, Gabriel Synnaeve, Alexandre Défossez:
From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion. - Rajat Vadiraj Dwaraknath, Tolga Ergen, Mert Pilanci:
Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs. - Alberto Bietti, Vivien Cabannes, Diane Bouchacourt, Hervé Jégou, Léon Bottou:
Birth of a Transformer: A Memory Viewpoint. - Hoomaan Maskan, Konstantinos Zygalakis, Alp Yurtsever:
A Variational Perspective on High-Resolution ODEs. - Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig, Oran Lang, Eran Ofek, Idan Szpektor:
What You See is What You Read? Improving Text-Image Alignment Evaluation. - Anuran Makur, Marios Mertzanidis, Alexandros Psomas, Athina Terzoglou:
On the Robustness of Mechanism Design under Total Variation Distance. - Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, Haoyi Xiong:
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models. - Tom M. George, Kimberly L. Stachenfeld, Caswell Barry, Claudia Clopath, Tomoki Fukai:
A generative model of the hippocampal formation trained with theta driven local learning rules. - James Queeney, Mouhacine Benosman:
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning. - Paul Geuchen, Felix Voigtländer:
Optimal approximation using complex-valued neural networks. - Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong:
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. - Leonard Papenmeier, Luigi Nardi, Matthias Poloczek:
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces. - Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj, Umut Simsekli:
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. - Haonan Wang, Xiaomeng Li:
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation. - Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang:
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. - Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa:
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models. - Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. - Xiaosen Wang, Kangheng Tong, Kun He:
Rethinking the Backward Propagation for Adversarial Transferability. - Yiting Dong, Yang Li, Dongcheng Zhao, Guobin Shen, Yi Zeng:
Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition. - Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato:
Compression with Bayesian Implicit Neural Representations. - Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, Eleni Triantafillou:
Towards Unbounded Machine Unlearning. - Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi:
Collaborative Learning via Prediction Consensus. - Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. - Honghao Wei, Xin Liu, Weina Wang, Lei Ying:
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks. - Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany:
Temporal Graph Benchmark for Machine Learning on Temporal Graphs. - Taehyeon Kim, Eric Lin, Junu Lee, Christian Lau, Vaikkunth Mugunthan:
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection. - Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian:
On the Generalization Properties of Diffusion Models. - Seokin Seo, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim:
Regularized Behavior Cloning for Blocking the Leakage of Past Action Information. - Yannai A. Gonczarowski, Gregory Kehne, Ariel D. Procaccia, Ben Schiffer, Shirley Zhang:
The Distortion of Binomial Voting Defies Expectation. - Xin Li, Sima Behpour, Thang Long Doan, Wenbin He, Liang Gou, Liu Ren:
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models. - Austin Watkins, Enayat Ullah, Thanh Nguyen-Tang, Raman Arora:
Optimistic Rates for Multi-Task Representation Learning. - Mostafa Dehghani, Basil Mustafa, Josip Djolonga, Jonathan Heek, Matthias Minderer, Mathilde Caron, Andreas Steiner, Joan Puigcerver, Robert Geirhos, Ibrahim M. Alabdulmohsin, Avital Oliver, Piotr Padlewski, Alexey A. Gritsenko, Mario Lucic, Neil Houlsby:
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. - Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun:
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. - Francis Ward, Francesca Toni, Francesco Belardinelli, Tom Everitt:
Honesty Is the Best Policy: Defining and Mitigating AI Deception. - Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, Yoshua Bengio:
Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context. - Yan Liu, Xiaokang Chen, Yan Gao, Zhe Su, Fengji Zhang, Daoguang Zan, Jian-Guang Lou, Pin-Yu Chen, Tsung-Yi Ho:
Uncovering and Quantifying Social Biases in Code Generation. - Yan Zhuang, Qi Liu, Guanhao Zhao, Zhenya Huang, Weizhe Huang, Zachary A. Pardos, Enhong Chen, Jinze Wu, Xin Li:
A Bounded Ability Estimation for Computerized Adaptive Testing. - Samuel Dooley, Gurnoor Singh Khurana, Chirag Mohapatra, Siddartha V. Naidu, Colin White:
ForecastPFN: Synthetically-Trained Zero-Shot Forecasting. - Fabian Zaiser, Andrzej S. Murawski, Chih-Hao Luke Ong:
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach. - Yinshuang Xu, Jiahui Lei, Kostas Daniilidis:
SE(3) Equivariant Convolution and Transformer in Ray Space. - Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David D. Cox, Yiming Yang, Chuang Gan:
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. - Weiliang Tang, Biqi Yang, Xianzhi Li, Yun-Hui Liu, Pheng-Ann Heng, Chi-Wing Fu:
Prototypical Variational Autoencoder for 3D Few-shot Object Detection. - David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon:
Double Gumbel Q-Learning. - Jiangxing Wang, Deheng Ye, Zongqing Lu:
Mutual-Information Regularized Multi-Agent Policy Iteration. - Xue Yan, Jiaxian Guo, Xingzhou Lou, Jun Wang, Haifeng Zhang, Yali Du:
An Efficient End-to-End Training Approach for Zero-Shot Human-AI Coordination.