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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. - Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas A. Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm:
Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games. - James Oldfield, Christos Tzelepis, Yannis Panagakis, Mihalis Nicolaou, Ioannis Patras:
Parts of Speech-Grounded Subspaces in Vision-Language Models. - Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas J. A. Harvey:
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. - Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt:
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent. - Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy:
Epistemic Neural Networks. - Peiyan Dong, Zhenglun Kong, Xin Meng, Pinrui Yu, Yifan Gong, Geng Yuan, Hao Tang, Yanzhi Wang:
HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception. - Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park:
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields. - Yatong Sun, Bin Wang, Zhu Sun, Xiaochun Yang, Yan Wang:
Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation. - Silviu Pitis:
Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewards. - Haotian Xue, Alexandre Araujo, Bin Hu, Yongxin Chen:
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability. - Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu:
InstanT: Semi-supervised Learning with Instance-dependent Thresholds. - Junlin Wu, Andrew Clark, Yiannis Kantaros, Yevgeniy Vorobeychik:
Neural Lyapunov Control for Discrete-Time Systems. - Aditya Chattopadhyay, Ryan Pilgrim, René Vidal:
Information Maximization Perspective of Orthogonal Matching Pursuit with Applications to Explainable AI. - Guan Wang, Yuhao Sun, Sijie Cheng, Sen Song:
Evolving Connectivity for Recurrent Spiking Neural Networks. - Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low:
Bayesian Optimization with Cost-varying Variable Subsets. - Andong Wang, Chao Li, Mingyuan Bai, Zhong Jin, Guoxu Zhou, Qibin Zhao:
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks. - Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Mehran Kazemi, Najoung Kim, He He:
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. - Jacob P. Portes, Alexander Trott, Sam Havens, Daniel King, Abhinav Venigalla, Moin Nadeem, Nikhil Sardana, Daya Khudia, Jonathan Frankle:
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining. - Xiao Zang, Miao Yin, Jinqi Xiao, Saman A. Zonouz, Bo Yuan:
GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search. - Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples. - Zhaozhi Qian, Robert Davis, Mihaela van der Schaar:
Synthcity: a benchmark framework for diverse use cases of tabular synthetic data. - Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar:
SOAR: Improved Indexing for Approximate Nearest Neighbor Search. - Pha A. Nguyen, Kha Gia Quach, Kris Kitani, Khoa Luu:
Type-to-Track: Retrieve Any Object via Prompt-based Tracking. - Stratis Tsirtsis, Manuel Gomez Rodriguez:
Finding Counterfactually Optimal Action Sequences in Continuous State Spaces. - Yu Pan, Ye Yuan, Yichun Yin, Zenglin Xu, Lifeng Shang, Xin Jiang, Qun Liu:
Reusing Pretrained Models by Multi-linear Operators for Efficient Training. - AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca A. Thiede, Anshul Kundaje, Alán Aspuru-Guzik:
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design. - Paul Yoo, Jiaxian Guo, Yutaka Matsuo, Shixiang Shane Gu:
DreamSparse: Escaping from Plato's Cave with 2D Diffusion Model Given Sparse Views. - Zhenyu Zhu, Francesco Locatello, Volkan Cevher:
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling. - Kai Zhao, Qiyu Kang, Yang Song, Rui She, Sijie Wang, Wee Peng Tay:
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach. - Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin:
A Path to Simpler Models Starts With Noise. - Zirui Liu, Guanchu Wang, Shaochen Zhong, Zhaozhuo Xu, Daochen Zha, Ruixiang (Ryan) Tang, Zhimeng Stephen Jiang, Kaixiong Zhou, Vipin Chaudhary, Shuai Xu, Xia Hu:
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model. - Yuyang Qiu, Uday V. Shanbhag, Farzad Yousefian:
Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization. - Michael Noukhovitch, Samuel Lavoie, Florian Strub, Aaron C. Courville:
Language Model Alignment with Elastic Reset. - Junyi Li, Heng Huang:
Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning. - Josephine Lamp, Mark Derdzinski, Christopher Hannemann, Joost van der Linden, Lu Feng, Tianhao Wang, David E. Evans:
GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces. - Oscar Michel, Anand Bhattad, Eli VanderBilt, Ranjay Krishna, Aniruddha Kembhavi, Tanmay Gupta:
OBJECT 3DIT: Language-guided 3D-aware Image Editing. - Tianyu Liu, Qitan Lv, Jie Wang, Shuling Yang, Hanzhu Chen:
Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction. - Royi Rassin, Eran Hirsch, Daniel Glickman, Shauli Ravfogel, Yoav Goldberg, Gal Chechik:
Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment. - Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári:
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. - Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong:
Two-Stage Learning to Defer with Multiple Experts. - Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon:
A Computationally Efficient Sparsified Online Newton Method. - Rainer Engelken:
SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks. - Senthil Purushwalkam, Nikhil Naik:
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image. - Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Jia Xu, Yanbo Feng, Qi Long, Li Shen:
Fair Canonical Correlation Analysis. - Zhiqing Sun, Yiming Yang:
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization. - George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem:
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. - Zhiyong Wang, Jize Xie, Xutong Liu, Shuai Li, John C. S. Lui:
Online Clustering of Bandits with Misspecified User Models. - Gehua Ma, Runhao Jiang, Rui Yan, Huajin Tang:
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes. - Soumya Basu, Abishek Sankararaman:
Double Auctions with Two-sided Bandit Feedback. - Juanhui Li, Harry Shomer, Haitao Mao, Shenglai Zeng, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin:
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking. - Seongsu Bae, Daeun Kyung, Jaehee Ryu, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei Ji, Eric I-Chao Chang, Tackeun Kim, Edward Choi:
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images. - Tianyi Chen, Qidi Wang, Zhen Dong, Liwei Shen, Xin Peng:
Enhancing Robot Program Synthesis Through Environmental Context. - Quanyi Li, Zhenghao Mark Peng, Lan Feng, Zhizheng Liu, Chenda Duan, Wenjie Mo, Bolei Zhou:
ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling. - Haobo Zhang, Junyuan Hong, Yuyang Deng, Mehrdad Mahdavi, Jiayu Zhou:
Understanding Deep Gradient Leakage via Inversion Influence Functions. - Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji:
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization. - Saghar Adler, Vijay G. Subramanian:
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space. - Xiao Luo, Haixin Wang, Zijie Huang, Huiyu Jiang, Abhijeet Gangan, Song Jiang, Yizhou Sun:
CARE: Modeling Interacting Dynamics Under Temporal Environmental Variation. - Vedant Nanda, Till Speicher, John P. Dickerson, Krishna P. Gummadi, Soheil Feizi, Adrian Weller:
Diffused Redundancy in Pre-trained Representations. - Mohammadamin Tavakoli, Pierre Baldi, Ann Marie Carlton, Yin Ting T. Chiu, Alexander Shmakov, David Van Vranken:
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning. - Jules Berman, Benjamin Peherstorfer:
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. - Sara Pieri, Jose Renato Restom, Samuel Horváth, Hisham Cholakkal:
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. - Ajay Subramanian, Elena Sizikova, Najib J. Majaj, Denis G. Pelli:
Spatial-frequency channels, shape bias, and adversarial robustness. - Trung Dang, Jasper C. H. Lee, Maoyuan Raymond Song, Paul Valiant:
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond 1+α Moments. - Hanlin Zhu, Amy Zhang:
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability. - Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. - Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Driggs-Campbell, Payel Das, Lav R. Varshney:
Efficient Equivariant Transfer Learning from Pretrained Models. - Sattar Vakili, Julia Olkhovskaya:
Kernelized Reinforcement Learning with Order Optimal Regret Bounds. - Haochen Li, Rui Zhang, Hantao Yao, Xinkai Song, Yifan Hao, Yongwei Zhao, Ling Li, Yunji Chen:
Learning Domain-Aware Detection Head with Prompt Tuning. - Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari:
Parallel Sampling of Diffusion Models. - Tao Wang, Sylvia L. Herbert, Sicun Gao:
Fractal Landscapes in Policy Optimization. - Sander Beckers:
Moral Responsibility for AI Systems. - Jeffrey Li, Jieyu Zhang, Ludwig Schmidt, Alexander J. Ratner:
Characterizing the Impacts of Semi-supervised Learning for Weak Supervision. - Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi:
Logarithmic Bayes Regret Bounds. - Keji He, Chenyang Si, Zhihe Lu, Yan Huang, Liang Wang, Xinchao Wang:
Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation. - Sunipa Dev, Jaya Goyal, Dinesh Tewari, Shachi Dave, Vinodkumar Prabhakaran:
Building Socio-culturally Inclusive Stereotype Resources with Community Engagement. - Hao Liu, Wilson Yan, Pieter Abbeel:
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment. - Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa:
QuIP: 2-Bit Quantization of Large Language Models With Guarantees. - Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low:
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. - Yifan Xu, Mengdan Zhang, Chaoyou Fu, Peixian Chen, Xiaoshan Yang, Ke Li, Changsheng Xu:
Multi-modal Queried Object Detection in the Wild. - Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds: Characterization and Extensions. - Peiyao Xiao, Hao Ban, Kaiyi Ji:
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms. - Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu, Baoyuan Wu:
DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection. - Yukun Huang, Jianan Wang, Ailing Zeng, He Cao, Xianbiao Qi, Yukai Shi, Zheng-Jun Zha, Lei Zhang:
DreamWaltz: Make a Scene with Complex 3D Animatable Avatars. - Chuanruo Ning, Ruihai Wu, Haoran Lu, Kaichun Mo, Hao Dong:
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects. - Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Daniel Berenberg, Ian Fisk, Andrew M. Watkins, Stephen Ra, Richard Bonneau, Mohammed AlQuraishi:
OpenProteinSet: Training data for structural biology at scale. - Palak Jain, Iden Kalemaj, Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith:
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation. - Huy Nguyen, TrungTin Nguyen, Nhat Ho:
Demystifying Softmax Gating Function in Gaussian Mixture of Experts. - Hanlin Yang, Chao Yu, Peng Sun, Siji Chen:
Hybrid Policy Optimization from Imperfect Demonstrations. - Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. - Jungtaek Kim, Mingxuan Li, Oliver Hinder, Paul W. Leu:
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations. - Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish Tendulkar, Rishabh K. Iyer, Abir De:
Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks. - Wei Zheng, James Cheng Peng, Zeyuan Hou, Boyu Lyu, Mengfan Wang, Xuelong Mi, Shuoxuan Qiao, Yinan Wan, Guoqiang Yu:
NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei Image Segmentation. - Muxi Chen, Yu Li, Qiang Xu:
HiBug: On Human-Interpretable Model Debug. - Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, Ivan Dokmanic, David Belius:
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. - Alan Q. Wang, Minh Nguyen, Mert R. Sabuncu:
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head. - Yu Gui, Rina Barber, Cong Ma:
Conformalized matrix completion. - Yuyang Deng, Ilja Kuzborskij, Mehrdad Mahdavi:
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation. - Emaad Khwaja, Yun Song, Aaron Agarunov, Bo Huang:
CELLE-2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer. - Xiao Han, Yukang Cao, Kai Han, Xiatian Zhu, Jiankang Deng, Yi-Zhe Song, Tao Xiang, Kwan-Yee K. Wong:
HeadSculpt: Crafting 3D Head Avatars with Text. - Zhen Xiang, Zidi Xiong, Bo Li:
CBD: A Certified Backdoor Detector Based on Local Dominant Probability. - Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, Zhaoxiang Zhang:
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models. - Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal:
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. - Sangwoong Yoon, Frank C. Park, Gunsu S. Yun, Iljung Kim, Yung-Kyun Noh:
Variational Weighting for Kernel Density Ratios. - Odelia Melamed, Gilad Yehudai, Gal Vardi:
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces. - Xingang Guo, Darioush Keivan, Geir E. Dullerud, Peter J. Seiler, Bin Hu:
Complexity of Derivative-Free Policy Optimization for Structured H∞ Control. - Anh Nguyen, Nikos Karampatziakis, Weizhu Chen:
Meet in the Middle: A New Pre-training Paradigm. - Tejas Jayashankar, Gary C. F. Lee, Alejandro Lancho, Amir Weiss, Yury Polyanskiy, Gregory W. Wornell:
Score-based Source Separation with Applications to Digital Communication Signals. - Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun:
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint. - Ge Zheng, Bin Yang, Jiajin Tang, Hong-Yu Zhou, Sibei Yang:
DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models. - Tosca Lechner, Vinayak Pathak, Ruth Urner:
Adversarially Robust Learning with Uncertain Perturbation Sets. - Xiaoran Hao, Yash Jhaveri, Patrick Shafto:
Common Ground in Cooperative Communication. - Chao Li, Chen Gong, Qiang He, Xinwen Hou:
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control. - Huikang Liu, Xiao Li, Anthony Man-Cho So:
ReSync: Riemannian Subgradient-based Robust Rotation Synchronization. - Zhijian Zhou, Jie Ni, Jia-He Yao, Wei Gao:
On the Exploration of Local Significant Differences For Two-Sample Test. - Xiaolong Wang, Runsen Xu, Zhuofan Cui, Zeyu Wan, Yu Zhang:
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator. - Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W. Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Y. Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. - Quanqi Hu, Dixian Zhu, Tianbao Yang:
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. - Hao Wang, Jiajun Fan, Zhichao Chen, Haoxuan Li, Weiming Liu, Tianqiao Liu, Quanyu Dai, Yichao Wang, Zhenhua Dong, Ruiming Tang:
Optimal Transport for Treatment Effect Estimation. - Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher:
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks. - Xiangyu Sun, Oliver Schulte:
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing. - Wenxuan Zhang, Mahani Aljunied, Chang Gao, Yew Ken Chia, Lidong Bing:
M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models. - Anthony Fuller, Koreen Millard, James R. Green:
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders. - Yingqiang Ge, Wenyue Hua, Kai Mei, Jianchao Ji, Juntao Tan, Shuyuan Xu, Zelong Li, Yongfeng Zhang:
OpenAGI: When LLM Meets Domain Experts. - Ruofan Wu, Jiawei Qiao, Mingzhe Wu, Wen Yu, Ming Zheng, Tengfei Liu, Tianyi Zhang, Weiqiang Wang:
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions. - Shangtong Gui, Chenze Shao, Zhengrui Ma, Xishan Zhang, Yunji Chen, Yang Feng:
Non-autoregressive Machine Translation with Probabilistic Context-free Grammar. - Jing Zhang, Chi Zhang, Wenjia Wang, Bingyi Jing:
Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning. - Saeid Alavi Naeini, Raeid Saqur, Mozhgan Saeidi, John M. Giorgi, Babak Taati:
Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset. - Colin Bredenberg, Ezekiel Williams, Cristina Savin, Blake A. Richards, Guillaume Lajoie:
Formalizing locality for normative synaptic plasticity models. - Hongchao Zhang, Junlin Wu, Yevgeniy Vorobeychik, Andrew Clark:
Exact Verification of ReLU Neural Control Barrier Functions. - Sébastien Herbreteau, Emmanuel Moebel, Charles Kervrann:
Normalization-Equivariant Neural Networks with Application to Image Denoising. - Yao Liu, Pratik Chaudhari, Rasool Fakoor:
Budgeting Counterfactual for Offline RL. - Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang:
Federated Conditional Stochastic Optimization. - Muhammad Jehanzeb Mirza, Leonid Karlinsky, Wei Lin, Horst Possegger, Mateusz Kozinski, Rogério Feris, Horst Bischof:
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections. - Chunlin Yu, Ye Shi, Jingya Wang:
Contextually Affinitive Neighborhood Refinery for Deep Clustering. - Tom Monnier, Jake Austin, Angjoo Kanazawa, Alexei A. Efros, Mathieu Aubry:
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives. - Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu:
Learning Shared Safety Constraints from Multi-task Demonstrations. - Zhengxiang Shi, Aldo Lipani:
Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner. - Haiteng Zhao, Shengchao Liu, Chang Ma, Hannan Xu, Jie Fu, Zhihong Deng, Lingpeng Kong, Qi Liu:
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning. - Sungyub Kim, Kyungsu Kim, Eunho Yang:
GEX: A flexible method for approximating influence via Geometric Ensemble. - Dhawal Gupta, Yinlam Chow, Azamat Tulepbergenov, Mohammad Ghavamzadeh, Craig Boutilier:
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. - Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. - Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? - Victor Letzelter, Mathieu Fontaine, Mickaël Chen, Patrick Pérez, Slim Essid, Gaël Richard:
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis. - Aditya Hemant Shahane, Saripilli Swapna Manjiri, Ankesh Jain, Sandeep Kumar:
Graph of Circuits with GNN for Exploring the Optimal Design Space. - Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan:
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data. - Jaemin Cho, Abhay Zala, Mohit Bansal:
Visual Programming for Step-by-Step Text-to-Image Generation and Evaluation. - Ben Chugg, Santiago Cortes-Gomez, Bryan Wilder, Aaditya Ramdas:
Auditing Fairness by Betting. - Md Ashiqur Rahman, Raymond A. Yeh:
Truly Scale-Equivariant Deep Nets with Fourier Layers. - Jincheng Cao, Ruichen Jiang, Nazanin Abolfazli, Erfan Yazdandoost Hamedani, Aryan Mokhtari:
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem. - Ziyu Chen, Wei Zhu:
On the Implicit Bias of Linear Equivariant Steerable Networks. - Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Memory-Constrained Algorithms for Convex Optimization. - Scott Alexander Cameron, Arnu Pretorius, Stephen J. Roberts:
Nonparametric Boundary Geometry in Physics Informed Deep Learning. - Joe Suk, Samory Kpotufe:
Tracking Most Significant Shifts in Nonparametric Contextual Bandits. - An Zhang, Leheng Sheng, Zhibo Cai, Xiang Wang, Tat-Seng Chua:
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss. - Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne:
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. - Ziang Liu, Genggeng Zhou, Jeff He, Tobia Marcucci, Fei-Fei Li, Jiajun Wu, Yunzhu Li:
Model-Based Control with Sparse Neural Dynamics. - Shaokai Ye, Jessy Lauer, Mu Zhou, Alexander Mathis, Mackenzie W. Mathis:
AmadeusGPT: a natural language interface for interactive animal behavioral analysis. - Yuan Cheng, Jing Yang, Yingbin Liang:
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs. - Paul Mineiro, Steven R. Howard:
Time-uniform confidence bands for the CDF under nonstationarity. - Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure. - Konstantin Makarychev, Sayak Chakrabarty:
Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple! - Yi-Chung Chen, Hsi-Wen Chen, Shun-Gui Wang, Ming-Syan Chen:
SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning. - Ziyuan Ye, Rihan Huang, Qilin Wu, Quanying Liu:
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations. - Michael Crawshaw, Yajie Bao, Mingrui Liu:
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. - Anwar Said, Roza G. Bayrak, Tyler Derr, Mudassir Shabbir, Daniel Moyer, Catie Chang, Xenofon D. Koutsoukos:
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics. - Daniel Freund, Thodoris Lykouris, Wentao Weng:
Quantifying the Cost of Learning in Queueing Systems. - Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone:
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models. - Qing Su, Anton Netchaev, Hai Li, Shihao Ji:
FLSL: Feature-level Self-supervised Learning. - Dipam Goswami, Yuyang Liu, Bartlomiej Twardowski, Joost van de Weijer:
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning. - Aoyang Qin, Feng Gao, Qing Li, Song-Chun Zhu, Sirui Xie:
Learning non-Markovian Decision-Making from State-only Sequences. - Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu:
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts. - Garrett Bingham, Risto Miikkulainen:
Efficient Activation Function Optimization through Surrogate Modeling. - Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes:
Data Market Design through Deep Learning. - Xinhong Ma, Yiming Wang, Hao Liu, Tianyu Guo, Yunhe Wang:
When Visual Prompt Tuning Meets Source-Free Domain Adaptive Semantic Segmentation. - Qiuyu Wang, Zifan Shi, Kecheng Zheng, Yinghao Xu, Sida Peng, Yujun Shen:
Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase. - Zhecheng Yuan, Sizhe Yang, Pu Hua, Can Chang, Kaizhe Hu, Huazhe Xu:
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization. - Ahmed Khaled, Konstantin Mishchenko, Chi Jin:
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. - Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi, Andreas Krause:
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning. - Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma:
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. - Yunqi Shi, Ke Xue, Song Lei, Chao Qian:
Macro Placement by Wire-Mask-Guided Black-Box Optimization. - Yuchen Yan, Baoyu Jing, Lihui Liu, Ruijie Wang, Jinning Li, Tarek F. Abdelzaher, Hanghang Tong:
Reconciling Competing Sampling Strategies of Network Embedding. - Hamed Nilforoshan, Michael Moor, Yusuf H. Roohani, Yining Chen, Anja Surina, Michihiro Yasunaga, Sara Oblak, Jure Leskovec:
Zero-shot causal learning. - Ceyuan Yang, Qihang Zhang, Yinghao Xu, Jiapeng Zhu, Yujun Shen, Bo Dai:
Learning Modulated Transformation in GANs. - Xichen Ye, Xiaoqiang Li, Songmin Dai, Tong Liu, Yan Sun, Weiqin Tong:
Active Negative Loss Functions for Learning with Noisy Labels. - Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel:
Compositional Generalization from First Principles. - Zheng Chen, Yan-Pei Cao, Yuan-Chen Guo, Chen Wang, Ying Shan, Song-Hai Zhang:
PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas. - Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang, Guy Wolf, Ian Adelstein, Smita Krishnaswamy:
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction. - Xuyang Chen, Lin Zhao:
Finite-Time Analysis of Single-Timescale Actor-Critic. - Hanting Chen, Yunhe Wang, Jianyuan Guo, Dacheng Tao:
VanillaNet: the Power of Minimalism in Deep Learning. - Dominik Straub, Matthias Schultheis, Heinz Koeppl, Constantin A. Rothkopf:
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs. - Prateek Yadav, Derek Tam, Leshem Choshen, Colin A. Raffel, Mohit Bansal:
TIES-Merging: Resolving Interference When Merging Models. - Haotian Xue, Antonio Torralba, Josh Tenenbaum, Dan Yamins, Yunzhu Li, Hsiao-Yu Tung:
3D-IntPhys: Towards More Generalized 3D-grou