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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 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 (Henry) 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 D. 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 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 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-grounded Visual Intuitive Physics under Challenging Scenes. - Weijian Luo, Boya Zhang, Zhihua Zhang:
Entropy-based Training Methods for Scalable Neural Implicit Samplers. - Hyungjin Chung, Jeongsol Kim, Jong Chul Ye:
Direct Diffusion Bridge using Data Consistency for Inverse Problems. - Yuetian Weng, Mingfei Han, Haoyu He, Mingjie Li, Lina Yao, Xiaojun Chang, Bohan Zhuang:
Mask Propagation for Efficient Video Semantic Segmentation. - Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal:
Private Distribution Learning with Public Data: The View from Sample Compression. - Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, Jun Wang:
ChessGPT: Bridging Policy Learning and Language Modeling. - Joon-Hyeok Yim, Anna A. Gilbert:
Fitting trees to 𝓁1-hyperbolic distances. - Daolang Huang, Ayush Bharti, Amauri H. Souza, Luigi Acerbi, Samuel Kaski:
Learning Robust Statistics for Simulation-based Inference under Model Misspecification. - Jonathan Pilault, Mahan Fathi, Orhan Firat, Chris Pal, Pierre-Luc Bacon, Ross Goroshin:
Block-State Transformers. - David S. Watson, Joshua O'Hara, Niek Tax, Richard Mudd, Ido Guy:
Explaining Predictive Uncertainty with Information Theoretic Shapley Values. - Thoranna Bender, Simon Møe Sørensen, Alireza Kashani, Kristjan Eldjarn Hjorleifsson, Grethe Hyldig, Søren Hauberg, Serge J. Belongie, Frederik Warburg:
Learning to Taste: A Multimodal Wine Dataset. - Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro:
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning. - Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter:
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. - Muhammad Salman Ali, Yeongwoong Kim, Maryam Qamar, Sung-Chang Lim, Donghyun Kim, Chaoning Zhang, Sung-Ho Bae, Hui Yong Kim:
Towards Efficient Image Compression Without Autoregressive Models. - Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang:
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents. - Luke Travis, Kolyan Ray:
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior. - Zhibin Duan, Zhiyi Lv, Chaojie Wang, Bo Chen, Bo An, Mingyuan Zhou:
Few-shot Generation via Recalling Brain-Inspired Episodic-Semantic Memory. - Joseph T. Costello, Hisham Temmar, Luis Cubillos, Matthew Mender, Dylan Wallace, Matt S. Willsey, Parag G. Patil, Cynthia A. Chestek:
Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces. - Scott Pesme, Nicolas Flammarion:
Saddle-to-Saddle Dynamics in Diagonal Linear Networks. - Guanghui Yu, Wei Tang, Saumik Narayanan, Chien-Ju Ho:
Encoding Human Behavior in Information Design through Deep Learning. - Chen Cheng, Gary Cheng, John C. Duchi:
Collaboratively Learning Linear Models with Structured Missing Data. - Shashank Hegde, Sumeet Batra, K. R. Zentner, Gaurav S. Sukhatme:
Generating Behaviorally Diverse Policies with Latent Diffusion Models. - Rachael Hwee Ling Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet:
Incentives in Private Collaborative Machine Learning. - Xiang Wang, Hangjie Yuan, Shiwei Zhang, Dayou Chen, Jiuniu Wang, Yingya Zhang, Yujun Shen, Deli Zhao, Jingren Zhou:
VideoComposer: Compositional Video Synthesis with Motion Controllability. - Peng Cheng, Xianyuan Zhan, Zhi-Hao Wu, Wenjia Zhang, Youfang Lin, Shoucheng Song, Han Wang, Li Jiang:
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL. - Roey Magen, Ohad Shamir:
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks. - Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev:
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. - Paul-Edouard Sarlin, Eduard Trulls, Marc Pollefeys, Jan Hosang, Simon Lynen:
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding. - Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp:
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. - Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S. Yu:
Equal Opportunity of Coverage in Fair Regression. - Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Teaching for Multiple Learners. - Angelica Chen, David Dohan, David R. So:
EvoPrompting: Language Models for Code-Level Neural Architecture Search. - Zechuan Zhang, Li Sun, Zongxin Yang, Ling Chen, Yi Yang:
Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction. - Pum Jun Kim, Yoojin Jang, Jisu Kim, Jaejun Yoo:
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models. - Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding:
A Unified Detection Framework for Inference-Stage Backdoor Defenses. - Qinyi Chen, Negin Golrezaei, Djallel Bouneffouf:
Non-Stationary Bandits with Auto-Regressive Temporal Dependency. - Martin Ryner, Jan Kronqvist, Johan Karlsson:
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces. - Félix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Tom Barrett:
Combinatorial Optimization with Policy Adaptation using Latent Space Search. - Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, Lester Mackey:
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking. - Dongwei Pan, Long Zhuo, Jingtan Piao, Huiwen Luo, Wei Cheng, Yuxin Wang, Siming Fan, Shengqi Liu, Lei Yang, Bo Dai, Ziwei Liu, Chen Change Loy, Chen Qian, Wayne Wu, Dahua Lin, Kwan-Yee Lin:
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars. - Wei Jin, Haitao Mao, Zheng Li, Haoming Jiang, Chen Luo, Hongzhi Wen, Haoyu Han, Hanqing Lu, Zhengyang Wang, Ruirui Li, Zhen Li, Monica Xiao Cheng, Rahul Goutam, Haiyang Zhang, Karthik Subbian, Suhang Wang, Yizhou Sun, Jiliang Tang, Bing Yin, Xianfeng Tang:
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation. - Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty:
Adversarial Resilience in Sequential Prediction via Abstention. - Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli:
Simplicity Bias in 1-Hidden Layer Neural Networks. - Elysia Q. Smyers, Sydney M. Katz, Anthony Corso, Mykel J. Kochenderfer:
AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator. - Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric P. Xing, Kun Zhang:
Temporally Disentangled Representation Learning under Unknown Nonstationarity. - Ruichen Jiang, Aryan Mokhtari:
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization. - Tao Lei, Junwen Bai, Siddhartha Brahma, Joshua Ainslie, Kenton Lee, Yanqi Zhou, Nan Du, Vincent Y. Zhao, Yuexin Wu, Bo Li, Yu Zhang, Ming-Wei Chang:
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference. - Futoshi Futami, Masahiro Fujisawa:
Time-Independent Information-Theoretic Generalization Bounds for SGLD. - Saumya Gupta, Yikai Zhang, Xiaoling Hu, Prateek Prasanna, Chao Chen:
Topology-Aware Uncertainty for Image Segmentation. - Atli Kosson, Martin Jaggi:
Multiplication-Free Transformer Training via Piecewise Affine Operations. - Junren Chen, Jonathan Scarlett, Michael Ng, Zhaoqiang Liu:
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing. - Hyunin Lee, Yuhao Ding, Jongmin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi:
Tempo Adaptation in Non-stationary Reinforcement Learning. - Eric Hedlin, Gopal Sharma, Shweta Mahajan, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi:
Unsupervised Semantic Correspondence Using Stable Diffusion. - Zhenxing Ge, Zheng Xu, Tianyu Ding, Wenbin Li, Yang Gao:
Efficient Subgame Refinement for Extensive-form Games. - Yuanze Wang, Yichao Yan, Dianxi Shi, Wenhan Zhu, Jianqiang Xia, Jeff Tan, Songchang Jin, Ke Gao, Xiaobo Li, Xiaokang Yang:
NeRF-IBVS: Visual Servo Based on NeRF for Visual Localization and Navigation. - Kaiqi Jiang, Dhruv Malik, Yuanzhi Li:
How Does Adaptive Optimization Impact Local Neural Network Geometry? - Benno Krojer, Elinor Poole-Dayan, Vikram Voleti, Chris Pal, Siva Reddy:
Are Diffusion Models Vision-And-Language Reasoners? - Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu:
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. - Abdullah Alomar, Munther A. Dahleh, Sean Mann, Devavrat Shah:
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise. - Ruiying Lu, Yujie Wu, Long Tian, Dongsheng Wang, Bo Chen, Xiyang Liu, Ruimin Hu:
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection. - Yinan Liang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, Jiwen Lu:
MCUFormer: Deploying Vision Tranformers on Microcontrollers with Limited Memory. - Zhijie Deng, Peng Cui, Jun Zhu:
Towards Accelerated Model Training via Bayesian Data Selection. - Wanxing Chang, Ye Shi, Jingya Wang:
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels. - Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang (Atlas) Wang, Mingyuan Zhou:
In-Context Learning Unlocked for Diffusion Models. - Jindong Jiang, Fei Deng, Gautam Singh, Sungjin Ahn:
Object-Centric Slot Diffusion. - Dieterich Lawson, Michael Li, Scott W. Linderman:
NAS-X: Neural Adaptive Smoothing via Twisting. - Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao:
Reflexion: language agents with verbal reinforcement learning. - Kazuto Fukuchi, Jun Sakuma:
Demographic Parity Constrained Minimax Optimal Regression under Linear Model. - Vicente Vivanco Cepeda, Gaurav Kumar Nayak, Mubarak Shah:
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization. - Haonan Yan, Wenjing Zhang, Qian Chen, Xiaoguang Li, Wenhai Sun, Hui Li, Xiaodong Lin:
RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks. - Jiyoung Park, Ian Pelakh, Stephan Wojtowytsch:
Minimum norm interpolation by perceptra: Explicit regularization and implicit bias. - Zihan Chen, Howard H. Yang, Tony Q. S. Quek, Kai Fong Ernest Chong:
Spectral Co-Distillation for Personalized Federated Learning. - Jingjing Li, Wei Ji, Size Wang, Wenbo Li, Li Cheng:
DVSOD: RGB-D Video Salient Object Detection. - Sanghyun Son, Laura Yu Zheng, Ryan Sullivan, Yi-Ling Qiao, Ming C. Lin:
Gradient Informed Proximal Policy Optimization. - Di Wang, Jing Zhang, Bo Du, Minqiang Xu, Lin Liu, Dacheng Tao, Liangpei Zhang:
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model. - Hao Liu, Pieter Abbeel:
Blockwise Parallel Transformers for Large Context Models. - Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, Zhenkun Wang:
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization. - Babak Esmaeili, Robin Walters, Heiko Zimmermann, Jan-Willem van de Meent:
Topological Obstructions and How to Avoid Them. - Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro:
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. - Cong Wang, Jinshan Pan, Wei Wang, Jiangxin Dong, Mengzhu Wang, Yakun Ju, Junyang Chen:
PromptRestorer: A Prompting Image Restoration Method with Degradation Perception. - Chenze Shao, Zhengrui Ma, Min Zhang, Yang Feng:
Beyond MLE: Convex Learning for Text Generation. - Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Bandit Task Assignment with Unknown Processing Time. - Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre, Jesse Dodge, Alex Fang, Youngjae Yu, Ludwig Schmidt, William Yang Wang, Yejin Choi:
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text. - Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan:
Towards Self-Interpretable Graph-Level Anomaly Detection. - Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy Orsborn, Eli Shlizerman:
AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity. - Peiyan Dong, Lei Lu, Chao Wu, Cheng Lyu, Geng Yuan, Hao Tang, Yanzhi Wang:
PackQViT: Faster Sub-8-bit Vision Transformers via Full and Packed Quantization on the Mobile. - Jiarui Feng, Lecheng Kong, Hao Liu, Dacheng Tao, Fuhai Li, Muhan Zhang, Yixin Chen:
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman. - Qitong Gao, Ge Gao, Juncheng Dong, Vahid Tarokh, Min Chi, Miroslav Pajic:
Off-Policy Evaluation for Human Feedback. - Yash Bhalgat, Iro Laina, João F. Henriques, Andrea Vedaldi, Andrew Zisserman:
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion. - Yejiang Wang, Yuhai Zhao, Daniel Zhengkui Wang, Ling Li:
GALOPA: Graph Transport Learning with Optimal Plan Alignment. - Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi:
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. - Shizhe Ding, Boyang Xia, Dongbo Bu:
Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement. - Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. - Jacobus G. M. van der Linden, Mathijs de Weerdt, Emir Demirovic:
Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming. - Tan Zhu, Fei Dou, Xinyu Wang, Jin Lu, Jinbo Bi:
Polyhedron Attention Module: Learning Adaptive-order Interactions. - Leyla Biabani, Annika Hennes, Morteza Monemizadeh, Melanie Schmidt:
Faster Query Times for Fully Dynamic k-Center Clustering with Outliers. - Jing-Cheng Pang, Xinyu Yang, Si-Hang Yang, Xiong-Hui Chen, Yang Yu:
Natural Language Instruction-following with Task-related Language Development and Translation. - Haoxing Tian, Alex Olshevsky, Yannis Paschalidis:
Convergence of Actor-Critic with Multi-Layer Neural Networks. - Cyrus Cousins, Elita A. Lobo, Marek Petrik, Yair Zick:
Percentile Criterion Optimization in Offline Reinforcement Learning. - Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, Furu Wei:
TextDiffuser: Diffusion Models as Text Painters. - Ziyu Wang, Mike Zheng Shou, Mengmi Zhang:
Object-centric Learning with Cyclic Walks between Parts and Whole. - Aldo Pacchiano, Jonathan Lee, Emma Brunskill:
Experiment Planning with Function Approximation. - Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma:
White-Box Transformers via Sparse Rate Reduction. - Jianghui Wang, Yang Chen, Xingyu Xie, Cong Fang, Zhouchen Lin:
Task-Robust Pre-Training for Worst-Case Downstream Adaptation. - Rie Johnson, Tong Zhang:
Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training. - Samantha Chen, Yusu Wang:
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions. - Mengping Yang, Ceyuan Yang, Yichi Zhang, Qingyan Bai, Yujun Shen, Bo Dai:
Revisiting the Evaluation of Image Synthesis with GANs. - Shi Chen, Ming Jiang, Qi Zhao:
What Do Deep Saliency Models Learn about Visual Attention? - Valentino Delle Rose, Alexander Kozachinskiy, Cristobal Rojas, Mircea Petrache, Pablo Barceló:
Three Iterations of (d - 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points. - Sepidehsadat (Sepid) Hossieni, Mohammad Amin Shabani, Saghar Irandoust, Yasutaka Furukawa:
Puzzlefusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving. - Thao Nguyen, Yuheng Li, Utkarsh Ojha, Yong Jae Lee:
Visual Instruction Inversion: Image Editing via Image Prompting. - Jifan Zhang, Shuai Shao, Saurabh Verma, Robert D. Nowak:
Algorithm Selection for Deep Active Learning with Imbalanced Datasets. - Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao:
Federated Compositional Deep AUC Maximization. - Kaifu Wang, Efthymia Tsamoura, Dan Roth:
On Learning Latent Models with Multi-Instance Weak Supervision. - Leonard Tang, Dan Ley:
Degraded Polygons Raise Fundamental Questions of Neural Network Perception. - Blake Bordelon, Cengiz Pehlevan:
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks. - Kush Bhatia, Avanika Narayan, Christopher De Sa, Christopher Ré:
TART: A plug-and-play Transformer module for task-agnostic reasoning. - Carsten T. Lüth, Till J. Bungert, Lukas Klein, Paul F. Jaeger:
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment. - Fabian Paischer, Thomas Adler, Markus Hofmarcher, Sepp Hochreiter:
Semantic HELM: A Human-Readable Memory for Reinforcement Learning. - Farnood Salehi, Tunç Ozan Aydin, André Gaillard, Guglielmo Camporese, Yuxuan Wang:
Empowering Convolutional Neural Nets with MetaSin Activation. - Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? - Yiqun Duan, Charles Chau, Zhen Wang, Yu-Kai Wang, Chin-Teng Lin:
DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation. - Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang:
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. - Mohammad Jalali, Cheuk Ting Li, Farzan Farnia:
An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions. - Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash:
A Cross-Moment Approach for Causal Effect Estimation. - Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew K. Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo Jimenez Rezende, Daniel Zoran:
Combining Behaviors with the Successor Features Keyboard. - Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David A. Clifton, S. Kevin Zhou, Lawrence H. Staib, James S. Duncan:
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective. - Jiaxu Tian, Dapeng Zhi, Si Liu, Peixin Wang, Cheng Chen, Min Zhang:
Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks. - Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P. Vetrov:
Star-Shaped Denoising Diffusion Probabilistic Models. - Alessio Mazzetto, Eli Upfal:
An Adaptive Algorithm for Learning with Unknown Distribution Drift. - Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer:
QLoRA: Efficient Finetuning of Quantized LLMs. - Ping Guo, Xiangpeng Wei, Yue Hu, Baosong Yang, Dayiheng Liu, Fei Huang, Jun Xie:
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning. - Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Tester-Learners for Halfspaces: Universal Algorithms. - Zhiwei Hao, Jianyuan Guo, Kai Han, Han Hu, Chang Xu, Yunhe Wang:
Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale. - Julian Tanke, Oh-Hun Kwon, Felix B. Mueller, Andreas Doering, Jürgen Gall:
Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context. - Ningyi Liao, Siqiang Luo, Xiang Li, Jieming Shi:
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings. - Aaron Zweig, Loucas Pillaud-Vivien, Joan Bruna:
On Single-Index Models beyond Gaussian Data. - Xiaoxiao Sun, Nidham Gazagnadou, Vivek Sharma, Lingjuan Lyu, Hongdong Li, Liang Zheng:
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? - Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Lió, Yuguang Wang:
Graph Denoising Diffusion for Inverse Protein Folding. - Chaofan Ma, Yuhuan Yang, Chen Ju, Fei Zhang, Ya Zhang, Yanfeng Wang:
Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation. - Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt:
Stable and low-precision training for large-scale vision-language models. - Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, Mahesh Sathiamoorthy:
Recommender Systems with Generative Retrieval. - Jinghuan Shang, Michael S. Ryoo:
Active Vision Reinforcement Learning under Limited Visual Observability. - Eduardo Sany Laber, Lucas Murtinho:
Optimization of Inter-group criteria for clustering with minimum size constraints. - Sihan Xu, Ziqiao Ma, Yidong Huang, Honglak Lee, Joyce Chai:
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation. - Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins:
Bandit Social Learning under Myopic Behavior. - Rainer Engelken:
Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians. - Carlos Mougan, Richard Plant, Clare Teng, Marya Bazzi, Alvaro Cabrejas Egea, Ryan Sze-Yin Chan, David Salvador Jasin, Martin Stoffel, Kirstie J. Whitaker, Jules Manser:
How to Data in Datathons. - Théo Gnassounou, Rémi Flamary, Alexandre Gramfort:
Convolution Monge Mapping Normalization for learning on sleep data. - Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento:
Online learning of long-range dependencies. - Yueming Lyu:
Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization. - Nikos Kolotouros, Thiemo Alldieck, Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Fieraru, Cristian Sminchisescu:
DreamHuman: Animatable 3D Avatars from Text. - Daesung Kim, Hye Won Chung:
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization. - Mengzi Amy Guo, Donghao Ying, Javad Lavaei, Zuo-Jun Max Shen:
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand. - Bowen Li, Jiashun Wang, Yaoyu Hu, Chen Wang, Sebastian A. Scherer:
VoxDet: Voxel Learning for Novel Instance Detection. - Guangyuan Jiang, Manjie Xu, Song-Chun Zhu, Wenjuan Han, Chi Zhang, Yixin Zhu:
Evaluating and Inducing Personality in Pre-trained Language Models. - Christopher T. H. Teo, Milad Abdollahzadeh, Ngai-Man Cheung:
On Measuring Fairness in Generative Models. - Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li, Jinghui Chen:
IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI. - Wenhan Yang, Jingdong Gao, Baharan Mirzasoleiman:
Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks. - Darshan Chakrabarti, Jelena Diakonikolas, Christian Kroer:
Block-Coordinate Methods and Restarting for Solving Extensive-Form Games. - Jia Guo, Shuai Lu, Lize Jia, Weihang Zhang, Huiqi Li:
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction. - Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion:
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. - Chengsen Wang, Zirui Zhuang, Qi Qi, Jingyu Wang, Xingyu Wang, Haifeng Sun, Jianxin Liao:
Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection. - Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Zhenrui Li, Guojun Peng, Yue-Jiao Gong, Yining Ma, Zhiguang Cao:
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning. - Huafeng Kuang, Hong Liu, Yongjian Wu, Shin'ichi Satoh, Rongrong Ji:
Improving Adversarial Robustness via Information Bottleneck Distillation. - Xinyu Zhou, Pinxue Guo, Lingyi Hong, Jinglun Li, Wei Zhang, Weifeng Ge, Wenqiang Zhang:
Reading Relevant Feature from Global Representation Memory for Visual Object Tracking. - Eshaan Nichani, Alex Damian, Jason D. Lee:
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks. - Tiansheng Huang, Sihao Hu, Ka Ho Chow, Fatih Ilhan, Selim F. Tekin, Ling Liu:
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training. - Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Robust Lipschitz Bandits to Adversarial Corruptions. - Langzhang Liang, Xiangjing Hu, Zenglin Xu, Zixing Song, Irwin King:
Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily. - Ruth Dannenfelser, Jeffrey Zhong, Ran Zhang, Vicky Yao:
Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts. - Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, Fei Huang:
RRHF: Rank Responses to Align Language Models with Human Feedback. - Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Sparsity-Preserving Differentially Private Training of Large Embedding Models. - Marcus A. Triplett, Marta Gajowa, Hillel Adesnik, Liam Paninski:
Bayesian target optimisation for high-precision holographic optogenetics. - Roy Uziel, Or Dinari, Oren Freifeld:
From ViT Features to Training-free Video Object Segmentation via Streaming-data Mixture Models. - David Mayo, Jesse Cummings, Xinyu Lin, Dan Gutfreund, Boris Katz, Andrei Barbu:
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time. - Utkarsh Ojha, Yuheng Li, Anirudh Sundara Rajan, Yingyu Liang, Yong Jae Lee:
What Knowledge Gets Distilled in Knowledge Distillation? - Patric Bonnier, Harald Oberhauser, Zoltán Szabó:
Kernelized Cumulants: Beyond Kernel Mean Embeddings. - Aleksandar Stanic, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber:
Contrastive Training of Complex-Valued Autoencoders for Object Discovery. - Zacharia Issa, Blanka Horvath, Maud Lemercier, Cristopher Salvi:
Non-adversarial training of Neural SDEs with signature kernel scores. - Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, Kwan-Yee K. Wong:
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models. - Yan-Shuo Liang, Wu-Jun Li:
Loss Decoupling for Task-Agnostic Continual Learning. - Insu Jeon, Minui Hong, Junhyeog Yun, Gunhee Kim:
Federated Learning via Meta-Variational Dropout. - Xiran Fan, Chun-Hao Yang, Baba C. Vemuri:
Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space. - Yuzhang Shang, Zhihang Yuan, Yan Yan:
MIM4DD: Mutual Information Maximization for Dataset Distillation. - Woojin Cho, Kookjin Lee, Donsub Rim, Noseong Park:
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks. - Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang, Heng Huang:
Solving a Class of Non-Convex Minimax Optimization in Federated Learning. - Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar:
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. - Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Contextual Bandits and Imitation Learning with Preference-Based Active Queries. - George Ma, Yifei Wang, Yisen Wang:
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding. - Jae Sung Park, Jack Hessel, Khyathi Raghavi Chandu, Paul Pu Liang, Ximing Lu, Peter West, Youngjae Yu, Qiuyuan Huang, Jianfeng Gao, Ali Farhadi, Yejin Choi:
Localized Symbolic Knowledge Distillation for Visual Commonsense Models. - Mengcheng Lan, Xinjiang Wang, Yiping Ke, Jiaxing Xu, Litong Feng, Wayne Zhang:
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation. - Junjiao Tian, Yen-Cheng Liu, James Seale Smith, Zsolt Kira:
Fast Trainable Projection for Robust Fine-tuning. - Shengpu Tang, Jenna Wiens:
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. - Nimrah Mustafa, Aleksandar Bojchevski, Rebekka Burkholz:
Are GATs Out of Balance? - Zhongang Cai, Wanqi Yin, Ailing Zeng, Chen Wei, Qingping Sun, Wang Yanjun, Hui En Pang, Haiyi Mei, Mingyuan Zhang, Lei Zhang, Chen Change Loy, Lei Yang, Ziwei Liu:
SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation. - Dorian Baudry, Fabien Pesquerel, Rémy Degenne, Odalric-Ambrym Maillard:
Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits. - Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer:
SHAP-IQ: Unified Approximation of any-order Shapley Interactions. - Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar:
Towards Last-layer Retraining for Group Robustness with Fewer Annotations. - Zhongli Jiang, Dabao Zhang:
Analysis of Variance of Multiple Causal Networks. - Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, Sergey Kolesnikov:
Revisiting the Minimalist Approach to Offline Reinforcement Learning. - Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan:
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. - Arthur Pellegrino, N. Alex Cayco-Gajic, Angus Chadwick:
Low Tensor Rank Learning of Neural Dynamics. - Jinrang Jia, Zhenjia Li, Yifeng Shi:
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. - Manjie Xu, Guangyuan Jiang, Wei Liang, Chi Zhang, Yixin Zhu:
Active Reasoning in an Open-World Environment. - Alexander Tyurin, Peter Richtárik:
2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression. - Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, Karthik Narasimhan:
Tree of Thoughts: Deliberate Problem Solving with Large Language Models. - Nicolas Keriven, Samuel Vaiter:
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. - Hugo Cui, Lenka Zdeborová:
High-dimensional Asymptotics of Denoising Autoencoders. - Jack Lanchantin, Shubham Toshniwal, Jason Weston, Arthur Szlam, Sainbayar Sukhbaatar:
Learning to Reason and Memorize with Self-Notes. - Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei:
What can a Single Attention Layer Learn? A Study Through the Random Features Lens. - Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan:
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. - Susung Hong, Donghoon Ahn, Seungryong Kim:
Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation. - Vinod Raman, Unique Subedi, Ambuj Tewari:
On the Learnability of Multilabel Ranking. - Lecheng Kong, Jiarui Feng, Hao Liu, Dacheng Tao, Yixin Chen, Muhan Zhang:
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network. - Mark D. McDonnell, Dong Gong, Amin Parvaneh, Ehsan Abbasnejad, Anton van den Hengel:
RanPAC: Random Projections and Pre-trained Models for Continual Learning. - Xinyu Sun, Peihao Chen, Jugang Fan, Jian Chen, Thomas H. Li, Mingkui Tan:
FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation. - Yukun Qiu, Guo-Hao Xu, Wei-Shi Zheng:
Inner-Outer Aware Reconstruction Model for Monocular 3D Scene Reconstruction. - Iulia Duta, Giulia Cassarà, Fabrizio Silvestri, Pietro Lió:
Sheaf Hypergraph Networks. - Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang:
f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences. - Zhiqun Zuo, Mahdi Khalili, Xueru Zhang:
Counterfactually Fair Representation. - Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli, Marcello Restelli:
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach. - Wenxuan Bao, Francesco Pittaluga, Vijay Kumar B. G, Vincent Bindschaedler:
DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning. - Yoni Kasten, Ohad Rahamim, Gal Chechik:
Point Cloud Completion with Pretrained Text-to-Image Diffusion Models. - Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew R. Walter:
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. - Niranjan Damera Venkata, Chiranjib Bhattacharyya:
Deep Recurrent Optimal Stopping. - Anindya Sarkar, Nathan Jacobs, Yevgeniy Vorobeychik:
A Partially-Supervised Reinforcement Learning Framework for Visual Active Search. - Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long:
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors. - Liyuan Liu, Chengyu Dong, Xiaodong Liu, Bin Yu, Jianfeng Gao:
Bridging Discrete and Backpropagation: Straight-Through and Beyond. - Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, Colin A. Raffel:
Distributed Inference and Fine-tuning of Large Language Models Over The Internet. - Jingyuan Sun, Mingxiao Li, Zijiao Chen, Yunhao Zhang, Shaonan Wang, Marie-Francine Moens:
Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities. - Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Frédo Durand, Bill Freeman, Vincent Sitzmann:
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision. - Tomas Vaskevicius, Lénaïc Chizat:
Computational Guarantees for Doubly Entropic Wasserstein Barycenters. - Yuxin Jia, Youfang Lin, Xinyan Hao, Yan Lin, Shengnan Guo, Huaiyu Wan:
WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting. - Matthew Lyon, Paul A. Armitage, Mauricio A. Álvarez:
Spatio-Angular Convolutions for Super-resolution in Diffusion MRI. - Changsheng Lv, Shuai Zhang, Yapeng Tian, Mengshi Qi, Huadong Ma:
Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning. - Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson:
Protein Design with Guided Discrete Diffusion. - Lyndon R. Duong, Eero P. Simoncelli, Dmitri B. Chklovskii, David Lipshutz:
Adaptive whitening with fast gain modulation and slow synaptic plasticity. - Dongjin Kim, Woojeong Kim, Suhyun Kim:
Tanh Works Better with Asymmetry. - Yihang Yao, Zuxin Liu, Zhepeng Cen, Jiacheng Zhu, Wenhao Yu, Tingnan Zhang, Ding Zhao:
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning. - Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alexander J. Smola, Xu Sun:
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. - Jinghan Zhang, Shiqi Chen, Junteng Liu, Junxian He:
Composing Parameter-Efficient Modules with Arithmetic Operation. - Yuyuan Li, Chaochao Chen, Yizhao Zhang, Weiming Liu, Lingjuan Lyu, Xiaolin Zheng, Dan Meng, Jun Wang:
UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition. - Elliott Ash, Naman Goel, Nianyun Li, Claudia Marangon, Peiyao Sun:
WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts. - Evangelia Gergatsouli, Christos Tzamos:
Weitzman's Rule for Pandora's Box with Correlations. - Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola:
Compositional Sculpting of Iterative Generative Processes. - Hatef Otroshi-Shahreza, Sébastien Marcel:
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network. - Jiachen Zhao, Tao Yu, Liang An, Yipeng Huang, Fang Deng, Qionghai Dai:
Triangulation Residual Loss for Data-efficient 3D Pose Estimation. - Junmin Zhong, Ruofan Wu, Jennie Si:
A Long N-step Surrogate Stage Reward for Deep Reinforcement Learning. - Chaoqi Chen, Luyao Tang, Yue Huang, Xiaoguang Han, Yizhou Yu:
CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation. - Jose Javier Gonzalez Ortiz, John V. Guttag, Adrian V. Dalca:
Scale-Space Hypernetworks for Efficient Biomedical Image Analysis. - Chaoqi Wang, Ziyu Ye, Zhe Feng, Ashwinkumar Badanidiyuru Varadaraja, Haifeng Xu:
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts. - Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. - Yige Hong, Qiaomin Xie, Yudong Chen, Weina Wang:
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption. - Ping Li, Xiaoyun Li:
Smooth Flipping Probability for Differential Private Sign Random Projection Methods. - Yanghao Li, Tongda Xu, Yan Wang, Jingjing Liu, Ya-Qin Zhang:
Idempotent Learned Image Compression with Right-Inverse. - Qi Wang, Yiqin Lv, Yang-He Feng, Zheng Xie, Jincai Huang:
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm. - Alistair White, Niki Kilbertus, Maximilian Gelbrecht, Niklas Boers:
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints. - Yiding Jiang, J. Zico Kolter, Roberta Raileanu:
On the Importance of Exploration for Generalization in Reinforcement Learning. - Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro:
Uniform Convergence with Square-Root Lipschitz Loss. - Sohir Maskey, Raffaele Paolino, Aras Bacho, Gitta Kutyniok:
A Fractional Graph Laplacian Approach to Oversmoothing. - Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury:
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. - Hong Chen, Xin Wang, Yuwei Zhou, Yijian Qin, Chaoyu Guan, Wenwu Zhu:
Joint Data-Task Generation for Auxiliary Learning. - Vinod Raman, Unique Subedi, Ambuj Tewari:
On Proper Learnability between Average- and Worst-case Robustness. - Riccardo Zamboni, Alberto Maria Metelli, Marcello Restelli:
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning. - Daniel Augusto de Souza, Alexander Nikitin, St John, Magnus Ross, Mauricio A. Álvarez, Marc Peter Deisenroth, João Paulo Pordeus Gomes, Diego Mesquita, César Lincoln C. Mattos:
Thin and deep Gaussian processes. - Kevin Ellis:
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language. - Anders Vestergaard Nørskov, Alexander Neergaard Zahid, Morten Mørup:
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion. - Eden Saig, Inbal Talgam-Cohen, Nir Rosenfeld:
Delegated Classification. - Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang:
PTQD: Accurate Post-Training Quantization for Diffusion Models. - Katie Luo, Zhenzhen Liu, Xiangyu Chen, Yurong You, Sagie Benaim, Cheng Perng Phoo, Mark E. Campbell, Wen Sun, Bharath Hariharan, Kilian Q. Weinberger:
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery. - John P. Dickerson, Seyed A. Esmaeili, Jamie H. Morgenstern, Claire Jie Zhang:
Doubly Constrained Fair Clustering. - Zongsheng Yue, Jianyi Wang, Chen Change Loy:
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting. - Yanchao Tan, Zihao Zhou, Hang Lv, Weiming Liu, Carl Yang:
WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding. - Yujia Zheng, Kun Zhang:
Generalizing Nonlinear ICA Beyond Structural Sparsity. - Hédi Hadiji, Sarah Sachs, Tim van Erven, Wouter M. Koolen:
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games. - Xiang Cheng, Bohan Wang, Jingzhao Zhang, Yusong Zhu:
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions. - Guy Hacohen, Daphna Weinshall:
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget. - Shenghuan Sun, Gregory M. Goldgof, Atul J. Butte, Ahmed M. Alaa:
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback. - Francesco Giannini, Stefano Fioravanti, Oguzhan Keskin, Alisia Maria Lupidi, Lucie Charlotte Magister, Pietro Lió, Pietro Barbiero:
Interpretable Graph Networks Formulate Universal Algebra Conjectures. - Xin Li, Dongze Lian, Zhihe Lu, Jiawang Bai, Zhibo Chen, Xinchao Wang:
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph. - Jiayu Wang, Kang Zhao, Yifeng Ma, Shiwei Zhang, Yingya Zhang, Yujun Shen, Deli Zhao, Jingren Zhou:
FaceComposer: A Unified Model for Versatile Facial Content Creation. - Ganyu Wang, Bin Gu, Qingsong Zhang, Xiang Li, Boyu Wang, Charles X. Ling:
A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning. - Zhengmian Hu, Heng Huang:
Optimization and Bayes: A Trade-off for Overparameterized Neural Networks. - Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman:
Understanding Social Reasoning in Language Models with Language Models. - Edward Raff, James Holt:
Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests. - Basile Confavreux, Poornima Ramesh, Pedro J. Gonçalves, Jakob H. Macke, Tim P. Vogels:
Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference. - Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar:
Joint Training of Deep Ensembles Fails Due to Learner Collusion. - Zih-Yun Chiu, Yi-Lin Tuan, William Yang Wang, Michael C. Yip:
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning. - Yite Wang, Jing Wu, Naira Hovakimyan, Ruoyu Sun:
Balanced Training for Sparse GANs. - Hanyang Zhao, Wenpin Tang, David D. Yao:
Policy Optimization for Continuous Reinforcement Learning. - Zhaoxi Chen, Fangzhou Hong, Haiyi Mei, Guangcong Wang, Lei Yang, Ziwei Liu:
PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation. - Weijie Tu, Weijian Deng, Tom Gedeon:
A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP). - Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding, De-Chuan Zhan, Han-Jia Ye:
Model Spider: Learning to Rank Pre-Trained Models Efficiently. - Georg Bökman, Fredrik Kahl:
Investigating how ReLU-networks encode symmetries. - Angela Zhou:
Optimal and Fair Encouragement Policy Evaluation and Learning. - Yao Ni, Piotr Koniusz:
NICE: NoIse-modulated Consistency rEgularization for Data-Efficient GANs. - Yang Yang, Yuxuan Zhang, Xin Song, Yi Xu:
Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning. - Rachel Redberg, Antti Koskela, Yu-Xiang Wang:
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. - Zhu Wang, Sourav Medya, Sathya N. Ravi:
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis. - Daiwen Sun, He Huang, Yao Li, Xinqi Gong, Qiwei Ye:
DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein. - Yanbo Chen, Weiwei Liu:
A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications. - Galen Pogoncheff, Jacob Granley, Michael Beyeler:
Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture. - Naman Deep Singh, Francesco Croce, Matthias Hein:
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models. - Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh, Enkelejda Kasneci:
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates. - Mingyuan Zhang, Huirong Li, Zhongang Cai, Jiawei Ren, Lei Yang, Ziwei Liu:
FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing. - Huizong Yang, Yuxin Sun, Ganesh Sundaramoorthi, Anthony J. Yezzi:
Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation. - Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, Wei-Ning Hsu:
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale. - Constantine Caramanis, Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos:
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method. - Taesik Gong, Yewon Kim, Taeckyung Lee, Sorn Chottananurak, Sung-Ju Lee:
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams. - Qi Zhu, Man Zhou, Jie Huang, Naishan Zheng, Hongzhi Gao, Chongyi Li, Yuan Xu, Feng Zhao:
FouriDown: Factoring Down-Sampling into Shuffling and Superposing.