default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID13th ICLR 2025: Singapore
- The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net 2025
Accept (Oral)
- Esben Kran, Jord Nguyen, Akash Kundu, Sami Jawhar, Jinsuk Park, Mateusz Maria Jurewicz:
DarkBench: Benchmarking Dark Patterns in Large Language Models. - Yantao Liu, Zijun Yao, Rui Min, Yixin Cao, Lei Hou, Juanzi Li:
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style. - Neil Rathi, Johannes Mehrer, Badr AlKhamissi, Taha Osama A Binhuraib, Nicholas M. Blauch, Martin Schrimpf:
TopoLM: brain-like spatio-functional organization in a topographic language model. - Fangyu Lei, Jixuan Chen, Yuxiao Ye, Ruisheng Cao, Dongchan Shin, Hongjin Su, Zhaoqing Suo, Hongcheng Gao, Wenjing Hu, Pengcheng Yin, Victor Zhong, Caiming Xiong, Ruoxi Sun, Qian Liu, Sida Wang, Tao Yu:
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows. - Jiyeon Kim, Hyunji Lee, Hyowon Cho, Joel Jang, Hyeonbin Hwang, Seungpil Won, Youbin Ahn, Dohaeng Lee, Minjoon Seo:
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition. - Yinan Zheng, Ruiming Liang, Kexin Zheng, Jinliang Zheng, Liyuan Mao, Jianxiong Li, Weihao Gu, Rui Ai, Shengbo Eben Li, Xianyuan Zhan, Jingjing Liu:
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance. - Dixant Mittal, Liwei Kang, Wee Sun Lee:
Learning to Search from Demonstration Sequences. - Maojia Song, Shang Hong Sim, Rishabh Bhardwaj, Hai Leong Chieu, Navonil Majumder, Soujanya Poria:
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse. - Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar:
MAP: Multi-Human-Value Alignment Palette. - Siyu Chen, Beining Wu, Miao Lu, Zhuoran Yang, Tianhao Wang:
Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model. - Daniel Paleka, Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Vineeth Bhat, Adam Shen, Evan Wang, Florian Tramèr:
Consistency Checks for Language Model Forecasters. - Dongyoung Kim, Kimin Lee, Jinwoo Shin, Jaehyung Kim:
Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment. - Chen Jiang, Jiahui An, Yating Liu, Ni Ji:
Brain Bandit: A Biologically Grounded Neural Network for Efficient Control of Exploration. - Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro:
MaestroMotif: Skill Design from Artificial Intelligence Feedback. - Xingyu Su, Haiyang Yu, Degui Zhi, Shuiwang Ji:
Learning to Discover Regulatory Elements for Gene Expression Prediction. - Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov:
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models. - João Loula, Benjamin LeBrun, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Alexander K. Lew, Tim Vieira, Timothy J. O'Donnell:
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo. - Tanishq Kumar, Zachary Ankner, Benjamin Frederick Spector, Blake Bordelon, Niklas Muennighoff, Mansheej Paul, Cengiz Pehlevan, Christopher Ré, Aditi Raghunathan:
Scaling Laws for Precision. - Sachin Goyal, Christina Baek, J. Zico Kolter, Aditi Raghunathan:
Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance. - Zhenrui Yue, Honglei Zhuang, Aijun Bai, Kai Hui, Rolf Jagerman, Hansi Zeng, Zhen Qin, Dong Wang, Xuanhui Wang, Michael Bendersky:
Inference Scaling for Long-Context Retrieval Augmented Generation. - Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar:
Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning. - Jiachen T. Wang, Dawn Song, James Zou, Prateek Mittal, Ruoxi Jia:
Capturing the Temporal Dependence of Training Data Influence. - Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy:
Self-Improvement in Language Models: The Sharpening Mechanism. - Jiachen T. Wang, Prateek Mittal, Dawn Song, Ruoxi Jia:
Data Shapley in One Training Run. - Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun:
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. - Junsol Kim, James Evans, Aaron Schein:
Linear Representations of Political Perspective Emerge in Large Language Models. - Nguyen Nhat Minh, Andrew Baker, Clement Neo, Allen G. Roush, Andreas Kirsch, Ravid Shwartz-Ziv:
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs. - Jonas Linkerhägner, Cheng Shi, Ivan Dokmanic:
Joint Graph Rewiring and Feature Denoising via Spectral Resonance. - Yuheng Zhang, Dian Yu, Baolin Peng, Linfeng Song, Ye Tian, Mingyue Huo, Nan Jiang, Haitao Mi, Dong Yu:
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning. - Yibo Yang, Justus C. Will, Stephan Mandt:
Progressive Compression with Universally Quantized Diffusion Models. - Erwan Fagnou, Paul Caillon, Blaise Delattre, Alexandre Allauzen:
Accelerated training through iterative gradient propagation along the residual path. - Site Bai, Brian Bullins:
Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity. - Keir Adams, Kento Abeywardane, Jenna C. Fromer, Connor W. Coley:
ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design. - Christopher Fifty, Ronald Guenther Junkins, Dennis Duan, Aniketh Iyengar, Jerry Weihong Liu, Ehsan Amid, Sebastian Thrun, Christopher Ré:
Restructuring Vector Quantization with the Rotation Trick. - Thomas Bush, Stephen Chung, Usman Anwar, Adrià Garriga-Alonso, David Krueger:
Interpreting Emergent Planning in Model-Free Reinforcement Learning. - Zhitong Xu, Haitao Wang, Jeff M. Phillips, Shandian Zhe:
Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization. - Florian E. Dorner, Vivian Yvonne Nastl, Moritz Hardt:
Limits to scalable evaluation at the frontier: LLM as judge won't beat twice the data. - Alex Iacob, Lorenzo Sani, Meghdad Kurmanji, William F. Shen, Xinchi Qiu, Dongqi Cai, Yan Gao, Nicholas Donald Lane:
DEPT: Decoupled Embeddings for Pre-training Language Models. - Jingchu Gai, Yiheng Du, Bohang Zhang, Haggai Maron, Liwei Wang:
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks. - Litu Rout, Yujia Chen, Nataniel Ruiz, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu:
RB-Modulation: Training-Free Stylization using Reference-Based Modulation. - Michael T. Matthews, Michael Beukman, Chris Lu, Jakob Nicolaus Foerster:
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks. - Po-Wei Huang, Pei-Chiun Peng, Hung Guei, Ti-Rong Wu:
OptionZero: Planning with Learned Options. - Vitalis Vosylius, Edward Johns:
Instant Policy: In-Context Imitation Learning via Graph Diffusion. - Andreas Christian Schneider, Valentin Neuhaus, David Alexander Ehrlich, Abdullah Makkeh, Alexander S. Ecker, Viola Priesemann, Michael Wibral:
What should a neuron aim for? Designing local objective functions based on information theory. - Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt:
Cross-Entropy Is All You Need To Invert the Data Generating Process. - Riccardo Grazzi, Julien Siems, Arber Zela, Jörg K. H. Franke, Frank Hutter, Massimiliano Pontil:
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. - Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu:
Attention as a Hypernetwork. - Juno Kim, Taiji Suzuki:
Transformers Provably Solve Parity Efficiently with Chain of Thought. - T. Konstantin Rusch, Daniela Rus:
Oscillatory State-Space Models. - Seunghun Lee, Jinyoung Park, Jaewon Chu, Minseo Yoon, Hyunwoo J. Kim:
Latent Bayesian Optimization via Autoregressive Normalizing Flows. - Guancheng Wan, Zitong Shi, Wenke Huang, Guibin Zhang, Dacheng Tao, Mang Ye:
Energy-based Backdoor Defense Against Federated Graph Learning. - Alihan Hüyük, Xinnuo Xu, Jacqueline R. M. A. Maasch, Aditya V. Nori, Javier González:
Reasoning Elicitation in Language Models via Counterfactual Feedback. - Maxence Faldor, Antoine Cully:
CAX: Cellular Automata Accelerated in JAX. - Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Küçükbenli, Arash Vahdat, Karsten Kreis:
Proteina: Scaling Flow-based Protein Structure Generative Models. - Kacper Wyrwal, Andreas Krause, Viacheslav Borovitskiy:
Residual Deep Gaussian Processes on Manifolds. - Vinh Tong, Dung-Trung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert:
Learning to Discretize Denoising Diffusion ODEs. - Huayu Chen, Hang Su, Peize Sun, Jun Zhu:
Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment. - Shiyu Wang, Jiawei Li, Xiaoming Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Shengtong Ju, Zhixuan Chu, Ming Jin:
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis. - Shilin Xu, Haobo Yuan, Qingyu Shi, Lu Qi, Jingbo Wang, Yibo Yang, Yining Li, Kai Chen, Yunhai Tong, Bernard Ghanem, Xiangtai Li, Ming-Hsuan Yang:
RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything. - Jingjing Gong, Yu Pei, Siyu Long, Yuxuan Song, Zhe Zhang, Wenhao Huang, Ziyao Cao, Shuyi Zhang, Hao Zhou, Wei-Ying Ma:
Steering Protein Family Design through Profile Bayesian Flow. - Ziwei Yang, Zheng Chen, Xin Liu, Rikuto Kotoge, Peng Chen, Yasuko Matsubara, Yasushi Sakurai, Jimeng Sun:
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation. - Sungyoon Kim, Aaron Mishkin, Mert Pilanci:
Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality. - Tianxiang Gao, Siyuan Sun, Hailiang Liu, Hongyang Gao:
Global Convergence in Neural ODEs: Impact of Activation Functions. - Chi-Heng Lin, Shangqian Gao, James Seale Smith, Abhishek Patel, Shikhar Tuli, Yilin Shen, Hongxia Jin, Yen-Chang Hsu:
MoDeGPT: Modular Decomposition for Large Language Model Compression. - Mrinal Mathur, Barak A. Pearlmutter, Sergey M. Plis:
MIND over Body: Adaptive Thinking using Dynamic Computation. - Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, Ji-Rong Wen:
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions. - Jui-Nan Yen, Si Si, Zhao Meng, Felix X. Yu, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh, Sanjiv Kumar:
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization. - Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu:
Scaling and evaluating sparse autoencoders. - Xiangyu Peng, Congying Xia, Xinyi Yang, Caiming Xiong, Chien-Sheng Wu, Chen Xing:
ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement. - Jindou Jia, Zihan Yang, Meng Wang, Kexin Guo, Jianfei Yang, Xiang Yu, Lei Guo:
Feedback Favors the Generalization of Neural ODEs. - Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, Kaixiang Lin:
Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs. - Armin W. Thomas, Rom N. Parnichkun, Alexander Amini, Stefano Massaroli, Michael Poli:
STAR: Synthesis of Tailored Architectures. - Javier Ferrando, Oscar Balcells Obeso, Senthooran Rajamanoharan, Neel Nanda:
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models. - Saket Tiwari, Omer Gottesman, George Konidaris:
Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces. - Hongkang Li, Yihua Zhang, Shuai Zhang, Pin-Yu Chen, Sijia Liu, Meng Wang:
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers. - Mohammad Bashiri, Luca Baroni, Ján Antolík, Fabian H. Sinz:
Learning and aligning single-neuron invariance manifolds in visual cortex. - Panagiotis Theodoropoulos, Nikolaos Komianos, Vincent Pacelli, Guan-Horng Liu, Evangelos A. Theodorou:
Feedback Schrödinger Bridge Matching. - Minghao Guo, Bohan Wang, Kaiming He, Wojciech Matusik:
TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes. - Boyu Gou, Ruohan Wang, Boyuan Zheng, Yanan Xie, Cheng Chang, Yiheng Shu, Huan Sun, Yu Su:
Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents. - Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel:
Progressive distillation induces an implicit curriculum. - Hao Sun, Yunyi Shen, Jean-Francois Ton:
Rethinking Reward Modeling in Preference-based Large Language Model Alignment. - Javier Abad, Konstantin Donhauser, Francesco Pinto, Fanny Yang:
Copyright-Protected Language Generation via Adaptive Model Fusion. - Han Lin, Jaemin Cho, Abhay Zala, Mohit Bansal:
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model. - Yu Feng, Ben Zhou, Weidong Lin, Dan Roth:
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models. - Zijie Geng, Jie Wang, Ziyan Liu, Siyuan Xu, Zhentao Tang, Shixiong Kai, Mingxuan Yuan, Jianye Hao, Feng Wu:
LaMPlace: Learning to Optimize Cross-Stage Metrics in Macro Placement. - Jiewen Hu, Thomas Zhu, Sean Welleck:
miniCTX: Neural Theorem Proving with (Long-)Contexts. - Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, James Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, et al.:
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions. - Tuo Xu:
Towards a Complete Logical Framework for GNN Expressiveness. - Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, Saining Xie:
Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think. - Yanzheng Chen, Jun Yu:
Classic but Everlasting: Traditional Gradient-Based Algorithms Converge Fast Even in Time-Varying Multi-Player Games. - Huaisheng Zhu, Teng Xiao, Vasant G. Honavar:
DSPO: Direct Score Preference Optimization for Diffusion Model Alignment. - Haiyang Liu, Xingchao Yang, Tomoya Akiyama, Yuantian Huang, Qiaoge Li, Shigeru Kuriyama, Takafumi Taketomi:
TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation. - Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, Jiangmiao Pang:
Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation. - Alexandros Hollender, Gilbert Maystre, Sai Ganesh Nagarajan:
The Complexity of Two-Team Polymatrix Games with Independent Adversaries. - Jian Wu, Linyi Yang, Dongyuan Li, Yuliang Ji, Manabu Okumura, Yue Zhang:
MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions. - Hexu Zhao, Haoyang Weng, Daohan Lu, Ang Li, Jinyang Li, Aurojit Panda, Saining Xie:
On Scaling Up 3D Gaussian Splatting Training. - Giuseppe Bruno, Federico Pasqualotto, Andrea Agazzi:
Emergence of meta-stable clustering in mean-field transformer models. - Arthur Jacot, Peter Súkeník, Zihan Wang, Marco Mondelli:
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse. - Pin Chen, Zexin Xu, Qing Mo, Hongjin Zhong, Fengyang Xu, Yutong Lu:
ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials. - Ricardo Buitrago Ruiz, Tanya Marwah, Albert Gu, Andrej Risteski:
On the Benefits of Memory for Modeling Time-Dependent PDEs. - Yichen Wu, Hongming Piao, Long-Kai Huang, Renzhen Wang, Wanhua Li, Hanspeter Pfister, Deyu Meng, Kede Ma, Ying Wei:
SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning. - Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber:
Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. - Yuxuan Sun, Yunlong Zhang, Yixuan Si, Chenglu Zhu, Kai Zhang, Zhongyi Shui, Jingxiong Li, Xuan Gong, Xinheng Lyu, Tao Lin, Lin Yang:
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration. - Ricardo Dominguez-Olmedo, Florian E. Dorner, Moritz Hardt:
Training on the Test Task Confounds Evaluation and Emergence. - Sungwon Kim, Yoonho Lee, Yunhak Oh, Namkyeong Lee, Sukwon Yun, Junseok Lee, Sein Kim, Carl Yang, Chanyoung Park:
Subgraph Federated Learning for Local Generalization. - Yan Scholten, Stephan Günnemann, Leo Schwinn:
A Probabilistic Perspective on Unlearning and Alignment for Large Language Models. - Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Aleksander Madry, Lilian Weng:
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering. - Johannes von Oswald, Seijin Kobayashi, Yassir Akram, Angelika Steger:
Learning Randomized Algorithms with Transformers. - Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, Yang Gao:
Data Scaling Laws in Imitation Learning for Robotic Manipulation. - Byoungwoo Park, Hyungi Lee, Juho Lee:
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series. - Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Jing Jiang, Min Lin:
Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates. - Yair Davidson, Nadav Dym:
On the Hölder Stability of Multiset and Graph Neural Networks. - Dehong Xu, Ruiqi Gao, Wenhao Zhang, Xue-Xin Wei, Ying Nian Wu:
On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding. - Ziqing Fan, Siyuan Du, Shengchao Hu, Pingjie Wang, Li Shen, Ya Zhang, Dacheng Tao, Yanfeng Wang:
Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection. - Geeling Chau, Christopher Wang, Sabera J. Talukder, Vighnesh Subramaniam, Saraswati Soedarmadji, Yisong Yue, Boris Katz, Andrei Barbu:
Population Transformer: Learning Population-level Representations of Neural Activity. - Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark:
KAN: Kolmogorov-Arnold Networks. - Wenjing Yan, Kai Zhang, Xiaolu Wang, Xuanyu Cao:
Problem-Parameter-Free Federated Learning. - Yongxing Zhang, Donglin Yang, Renjie Liao:
SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups. - Leheng Sheng, An Zhang, Yi Zhang, Yuxin Chen, Xiang Wang, Tat-Seng Chua:
Language Representations Can be What Recommenders Need: Findings and Potentials. - Qiushi Huang, Tom Ko, Zhan Zhuang, Lilian Tang, Yu Zhang:
HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models. - Shih-Hsin Wang, Yuhao Huang, Justin M. Baker, Yuan-En Sun, Qi Tang, Bao Wang:
A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules. - Yao Tong, Jiayuan Ye, Sajjad Zarifzadeh, Reza Shokri:
How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning. - Hanyu Wang, Saksham Suri, Yixuan Ren, Hao Chen, Abhinav Shrivastava:
LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior. - Zhuoxiao Chen, Junjie Meng, Mahsa Baktashmotlagh, Yonggang Zhang, Zi Huang, Yadan Luo:
MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection. - Zitong Yang, Neil Band, Shuangping Li, Emmanuel J. Candès, Tatsunori Hashimoto:
Synthetic continued pretraining. - Xiuwei Xu, Huangxing Chen, Linqing Zhao, Ziwei Wang, Jie Zhou, Jiwen Lu:
EmbodiedSAM: Online Segment Any 3D Thing in Real Time. - Eric Mazumdar, Kishan Panaganti, Laixi Shi:
Tractable Multi-Agent Reinforcement Learning through Behavioral Economics. - Sayan Banerjee, Krishna Balasubramanian, Promit Ghosal:
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent. - Gangwei Jiang, Caigao Jiang, Zhaoyi Li, Siqiao Xue, Jun Zhou, Linqi Song, Defu Lian, Ying Wei:
Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning. - Kevin Frans, Danijar Hafner, Sergey Levine, Pieter Abbeel:
One Step Diffusion via Shortcut Models. - Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali K. Thabet, Jonas Kohler:
Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment. - Zhiwei Zhang, Minhua Lin, Junjie Xu, Zongyu Wu, Enyan Dai, Suhang Wang:
Robustness Inspired Graph Backdoor Defense. - Song Tang, Wenxin Su, Yan Gan, Mao Ye, Jianwei Zhang, Xiatian Zhu:
Proxy Denoising for Source-Free Domain Adaptation. - Andy K. Zhang, Neil Perry, Riya Dulepet, Joey Ji, Celeste Menders, Justin W. Lin, Eliot Jones, Gashon Hussein, Samantha Liu, Donovan Julian Jasper, Pura Peetathawatchai, Ari Glenn, Vikram Sivashankar, Daniel Zamoshchin, Leo Glikbarg, Derek Askaryar, Haoxiang Yang, Aolin Zhang, Rishi Alluri, Nathan Tran, et al.:
Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models. - Mohamed El Amine Boudjoghra, Angela Dai, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Salman H. Khan, Fahad Shahbaz Khan:
Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation. - Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, Peter Henderson:
Safety Alignment Should be Made More Than Just a Few Tokens Deep. - Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai, Xiangchen Song, Mingming Gong, Guangyi Chen, Kun Zhang:
On the Identification of Temporal Causal Representation with Instantaneous Dependence. - Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jian-Guang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, Yansong Tang, Dongmei Zhang:
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct. - Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar:
Faster Cascades via Speculative Decoding. - Ali Shirali, Ariel D. Procaccia, Rediet Abebe:
The Hidden Cost of Waiting for Accurate Predictions. - Yi Ren, Danica J. Sutherland:
Learning Dynamics of LLM Finetuning. - Xiao Han, Saima Absar, Lu Zhang, Shuhan Yuan:
Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery. - Hannes Stärk, Bowen Jing, Tomas Geffner, Jason Yim, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis:
ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids. - Aaron Jiaxun Li, Satyapriya Krishna, Himabindu Lakkaraju:
More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness. - Tai Hoang, Huy Le, Philipp Becker, Ngo Anh Vien, Gerhard Neumann:
Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects. - Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron:
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. - Jianwen Jiang, Chao Liang, Jiaqi Yang, Gaojie Lin, Tianyun Zhong, Yanbo Zheng:
Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency. - Gaojie Lin, Jianwen Jiang, Chao Liang, Tianyun Zhong, Jiaqi Yang, Zerong Zheng, Yanbo Zheng:
CyberHost: A One-stage Diffusion Framework for Audio-driven Talking Body Generation. - Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, Omer Levy:
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model. - Peng Jin, Bo Zhu, Li Yuan, Shuicheng Yan:
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts. - Avik Pal, Max van Spengler, Guido Maria D'Amely di Melendugno, Alessandro Flaborea, Fabio Galasso, Pascal Mettes:
Compositional Entailment Learning for Hyperbolic Vision-Language Models. - Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, Razvan Ciuca, Tianyu Zhang, Gauthier Gidel, Aaron C. Courville:
Advantage Alignment Algorithms. - Lvmin Zhang, Anyi Rao, Maneesh Agrawala:
Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport. - Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Jie Shi, Xiang Wang, Xiangnan He, Tat-Seng Chua:
AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models. - Mathias Jackermeier, Alessandro Abate:
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL. - Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, Kun Wang, Yang Liu, Junfeng Fang, Yongbin Li:
On the Role of Attention Heads in Large Language Model Safety. - Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner:
Influence Functions for Scalable Data Attribution in Diffusion Models. - Lesi Chen, Chengchang Liu, Jingzhao Zhang:
Second-Order Min-Max Optimization with Lazy Hessians. - Gabriele Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause:
Composing Unbalanced Flows for Flexible Docking and Relaxation. - Mario Lino Valencia, Tobias Pfaff, Nils Thuerey:
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks. - Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust:
Training Language Models to Self-Correct via Reinforcement Learning. - Ximing Lu, Melanie Sclar, Skyler Hallinan, Niloofar Mireshghallah, Jiacheng Liu, Seungju Han, Allyson Ettinger, Liwei Jiang, Khyathi Raghavi Chandu, Nouha Dziri, Yejin Choi:
AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text. - Amin Nejatbakhsh, Victor Geadah, Alex H. Williams, David Lipshutz:
Comparing noisy neural population dynamics using optimal transport distances. - Zhenyi Zhang, Tiejun Li, Peijie Zhou:
Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport. - Renhao Wang, Kevin Frans, Pieter Abbeel, Sergey Levine, Alexei A. Efros:
Prioritized Generative Replay. - Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch:
The Geometry of Categorical and Hierarchical Concepts in Large Language Models. - Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi S. Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman:
Generator Matching: Generative modeling with arbitrary Markov processes. - Botao Ye, Sifei Liu, Haofei Xu, Xueting Li, Marc Pollefeys, Ming-Hsuan Yang, Songyou Peng:
No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images. - Badr Moufad, Yazid Janati, Lisa Bedin, Alain Oliviero Durmus, Randal Douc, Eric Moulines, Jimmy Olsson:
Variational Diffusion Posterior Sampling with Midpoint Guidance. - Jingyang Li, Jiachun Pan, Vincent Y. F. Tan, Kim-Chuan Toh, Pan Zhou:
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning. - Hanqiao Ye, Yuzhou Liu, Yangdong Liu, Shuhan Shen:
NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields. - Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller:
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models. - Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng, Yao Fu:
Retrieval Head Mechanistically Explains Long-Context Factuality. - Ziye Wang, Yiran Qin, Lin Zeng, Ruimao Zhang:
High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation. - Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, Furu Wei:
Differential Transformer. - Yongxian Wei, Zixuan Hu, Li Shen, Zhenyi Wang, Chun Yuan, Dacheng Tao:
Open-Vocabulary Customization from CLIP via Data-Free Knowledge Distillation. - Jaehun Jung, Faeze Brahman, Yejin Choi:
Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement. - Ziyue Li, Tianyi Zhou:
Your Mixture-of-Experts LLM Is Secretly an Embedding Model for Free. - Jie Zhang, Dongrui Liu, Chen Qian, Linfeng Zhang, Yong Liu, Yu Qiao, Jing Shao:
REEF: Representation Encoding Fingerprints for Large Language Models. - Hyun-Kyu Lee, Sung Whan Yoon:
Flat Reward in Policy Parameter Space Implies Robust Reinforcement Learning. - Parshin Shojaee, Kazem Meidani, Shashank Gupta, Amir Barati Farimani, Chandan K. Reddy:
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models. - Yiming Zhang, Jianfeng Chi, Hailey Nguyen, Kartikeya Upasani, Daniel M. Bikel, Jason E. Weston, Eric Michael Smith:
Backtracking Improves Generation Safety. - Chenbin Zhang, Zhiqiang Hu, Chuchu Jiang, Wen Chen, Jie Xu, Shaoting Zhang:
Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation. - Honghui Wang, Shiji Song, Gao Huang:
GridMix: Exploring Spatial Modulation for Neural Fields in PDE Modeling. - Yuxian Gu, Li Dong, Hongning Wang, Yaru Hao, Qingxiu Dong, Furu Wei, Minlie Huang:
Data Selection via Optimal Control for Language Models. - Cheng Lu, Yang Song:
Simplifying, Stabilizing and Scaling Continuous-time Consistency Models. - Yue Yang, Shuibo Zhang, Kaipeng Zhang, Yi Bin, Yu Wang, Ping Luo, Wenqi Shao:
Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping. - Yuda Song, Hanlin Zhang, Carson Eisenach, Sham M. Kakade, Dean P. Foster, Udaya Ghai:
Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models. - Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, Song Han:
SANA: Efficient High-Resolution Text-to-Image Synthesis with Linear Diffusion Transformers. - Chongyi Zheng, Jens Tuyls, Joanne Peng, Benjamin Eysenbach:
Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning. - Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang:
When Selection Meets Intervention: Additional Complexities in Causal Discovery. - Haian Jin, Hanwen Jiang, Hao Tan, Kai Zhang, Sai Bi, Tianyuan Zhang, Fujun Luan, Noah Snavely, Zexiang Xu:
LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias. - Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, Ricky T. Q. Chen:
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective. - Erik Wijmans, Brody Huval, Alexander Hertzberg, Vladlen Koltun, Philipp Krähenbühl:
Cut Your Losses in Large-Vocabulary Language Models. - Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, Chenglin Wu:
AFlow: Automating Agentic Workflow Generation. - Simon Schrodi, David T. Hoffmann, Max Argus, Volker Fischer, Thomas Brox:
Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models. - Xunhao Lai, Jianqiao Lu, Yao Luo, Yiyuan Ma, Xun Zhou:
FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference. - Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, Insup Lee:
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments. - Peng Xia, Siwei Han, Shi Qiu, Yiyang Zhou, Zhaoyang Wang, Wenhao Zheng, Zhaorun Chen, Chenhang Cui, Mingyu Ding, Linjie Li, Lijuan Wang, Huaxiu Yao:
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models. - Zhiyuan Weng, Guikun Chen, Wenguan Wang:
Do as We Do, Not as You Think: the Conformity of Large Language Models. - Takeru Miyato, Sindy Löwe, Andreas Geiger, Max Welling:
Artificial Kuramoto Oscillatory Neurons. - Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, Ling Liu:
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation. - Ching Lam Choi, Alexandre Duplessis, Serge J. Belongie:
Unlearning-based Neural Interpretations. - Zhengzhuo Xu, Bowen Qu, Yiyan Qi, Sinan Du, Chengjin Xu, Chun Yuan, Jian Guo:
ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding. - Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro:
Probabilistic Learning to Defer: Handling Missing Expert Annotations and Controlling Workload Distribution. - Zhuang Liu, Kaiming He:
A Decade's Battle on Dataset Bias: Are We There Yet? - Xin Gu, Yaojie Shen, Chenxi Luo, Tiejian Luo, Yan Huang, Yuewei Lin, Heng Fan, Libo Zhang:
Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding. - Jiajian Li, Qi Wang, Yunbo Wang, Xin Jin, Yang Li, Wenjun Zeng, Xiaokang Yang:
Open-World Reinforcement Learning over Long Short-Term Imagination. - Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Evan Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, et al.:
OLMoE: Open Mixture-of-Experts Language Models. - Zheyuan Zhang, Fengyuan Hu, Jayjun Lee, Freda Shi, Parisa Kordjamshidi, Joyce Chai, Ziqiao Ma:
Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference under Ambiguities. - Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloé Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross B. Girshick, Piotr Dollár, Christoph Feichtenhofer:
SAM 2: Segment Anything in Images and Videos. - Atsunobu Kotani, Ren Ng:
A Computational Framework for Modeling Emergence of Color Vision in the Human Brain. - Wei Chow, Jiageng Mao, Boyi Li, Daniel Seita, Vitor Campagnolo Guizilini, Yue Wang:
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding.
Accept (Spotlight)
- Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler:
How new data permeates LLM knowledge and how to dilute it. - Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li:
UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with Unified Multi-Objective Optimization. - Xinyi Liu, Yujie Wang, Fangcheng Fu, Xupeng Miao, Shenhan Zhu, Xiaonan Nie, Bin Cui:
NetMoE: Accelerating MoE Training through Dynamic Sample Placement. - Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy, Artem Babenko:
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks. - Jiangrong Shen, Qi Xu, Gang Pan, Badong Chen:
Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency. - Lianghui Zhu, Xinggang Wang, Xinlong Wang:
JudgeLM: Fine-tuned Large Language Models are Scalable Judges. - Thomas Tian, Kratarth Goel:
Direct Post-Training Preference Alignment for Multi-Agent Motion Generation Model Using Implicit Feedback from Pre-training Demonstrations. - Chenjia Bai, Yang Zhang, Shuang Qiu, Qiaosheng Zhang, Kang Xu, Xuelong Li:
Online Preference Alignment for Language Models via Count-based Exploration. - Weize Chen, Ziming You, Ran Li, Yitong Guan, Chen Qian, Chenyang Zhao, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun:
Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence. - Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar:
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. - Shaochen Zhong, Yifan Lu, Lize Shao, Bhargav Bhushanam, Xiaocong Du, Yixin Wan, Yucheng Shi, Daochen Zha, Yiwei Wang, Ninghao Liu, Kaixiong Zhou, Shuai Xu, Kai-Wei Chang, Louis Feng, Vipin Chaudhary, Xia Hu:
MQuAKE-Remastered: Multi-Hop Knowledge Editing Can Only Be Advanced with Reliable Evaluations. - Core Francisco Park, Ekdeep Singh Lubana, Hidenori Tanaka:
Competition Dynamics Shape Algorithmic Phases of In-Context Learning. - Han Yu, Hanrui Lyu, YiXun Xu, Charlie Windolf, Eric Kenji Lee, Fan Yang, Andrew M. Shelton, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Chandramouli Chandrasekaran, Nicholas A. Steinmetz, Liam Paninski, Cole Lincoln Hurwitz:
In vivo cell-type and brain region classification via multimodal contrastive learning. - Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws. - Baiting Luo, Ava Pettet, Aron Laszka, Abhishek Dubey, Ayan Mukhopadhyay:
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction. - Martin Rohbeck, Edward De Brouwer, Charlotte Bunne, Jan-Christian Huetter, Anne Biton, Kelvin Y. Chen, Aviv Regev, Romain Lopez:
Modeling Complex System Dynamics with Flow Matching Across Time and Conditions. - Chenliang Li, Siliang Zeng, Zeyi Liao, Jiaxiang Li, Dongyeop Kang, Alfredo García, Mingyi Hong:
Joint Reward and Policy Learning with Demonstrations and Human Feedback Improves Alignment. - Arvind Raghavan, Elias Bareinboim:
Counterfactual Realizability. - Zhichao Hou, MohamadAli Torkamani, Hamid Krim, Xiaorui Liu:
Robustness Reprogramming for Representation Learning. - Mufei Li, Viraj Shitole, Eli Chien, Changhai Man, Zhaodong Wang, Srinivas, Ying Zhang, Tushar Krishna, Pan Li:
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation. - Yu Ying Chiu, Liwei Jiang, Yejin Choi:
DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life. - Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu:
Learning-Augmented Frequent Directions. - Anastasiia Filippova, Angelos Katharopoulos, David Grangier, Ronan Collobert:
No Need to Talk: Asynchronous Mixture of Language Models. - Gabriel Wu, Jacob Hilton:
Estimating the Probabilities of Rare Outputs in Language Models. - Ryien Hosseini, Filippo Simini, Venkatram Vishwanath, Rebecca Willett, Henry Hoffmann:
Quality Measures for Dynamic Graph Generative Models. - Cassidy Laidlaw, Shivam Singhal, Anca D. Dragan:
Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking. - Jacob Morrison, Clara Na, Jared Fernandez, Tim Dettmers, Emma Strubell, Jesse Dodge:
Holistically Evaluating the Environmental Impact of Creating Language Models. - Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, James Zou:
Mixture-of-Agents Enhances Large Language Model Capabilities. - Sarah Wiegreffe, Oyvind Tafjord, Yonatan Belinkov, Hannaneh Hajishirzi, Ashish Sabharwal:
Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions. - Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan, Charlotte Peale, Udi Wieder:
Provable Uncertainty Decomposition via Higher-Order Calibration. - Ammar I Marvi, Nancy Kanwisher, Meenakshi Khosla:
Sparse components distinguish visual pathways & their alignment to neural networks. - Sheng Liu, Haotian Ye, James Zou:
Reducing Hallucinations in Large Vision-Language Models via Latent Space Steering. - Mansi Sakarvadia, Aswathy Ajith, Arham Mushtaq Khan, Nathaniel C. Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney:
Mitigating Memorization in Language Models. - Georgiana Dinu, Corey D. Barrett, Yi Xiang, Miguel Romero Calvo, Anna Currey, Xing Niu:
Effective post-training embedding compression via temperature control in contrastive training. - Mayukh Deb, Mainak Deb, N. Apurva Ratan Murty:
TopoNets: High performing vision and language models with brain-like topography. - Angelika Romanou, Negar Foroutan, Anna Sotnikova, Sree Harsha Nelaturu, Shivalika Singh, Rishabh Maheshwary, Micol Altomare, Zeming Chen, Mohamed A. Haggag, Snegha A, Alfonso Amayuelas, Azril Hafizi Amirudin, Danylo Boiko, Michael Chang, Jenny Chim, Gal Cohen, Aditya Kumar Dalmia, Abraham Diress, Sharad Duwal, Daniil Dzenhaliou, et al.:
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge. - Leonardo Ferreira Guilhoto, Paris Perdikaris:
Deep Learning Alternatives Of The Kolmogorov Superposition Theorem. - Yecheng Jason Ma, Joey Hejna, Chuyuan Fu, Dhruv Shah, Jacky Liang, Zhuo Xu, Sean Kirmani, Peng Xu, Danny Driess, Ted Xiao, Osbert Bastani, Dinesh Jayaraman, Wenhao Yu, Tingnan Zhang, Dorsa Sadigh, Fei Xia:
Vision Language Models are In-Context Value Learners. - Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti:
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding. - Shashata Sawmya, Linghao Kong, Ilia Markov, Dan Alistarh, Nir Shavit:
Wasserstein Distances, Neuronal Entanglement, and Sparsity. - Katie Matton, Robert Osazuwa Ness, John V. Guttag, Emre Kiciman:
Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations. - Shuang Liang, Guido Montúfar:
Implicit Bias of Mirror Flow for Shallow Neural Networks in Univariate Regression. - Bill Yuchen Lin, Yuntian Deng, Khyathi Raghavi Chandu, Abhilasha Ravichander, Valentina Pyatkin, Nouha Dziri, Ronan Le Bras, Yejin Choi:
WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild. - Shaocong Ma, Heng Huang:
Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations. - Daogao Liu, Kunal Talwar:
Adaptive Batch Size for Privately Finding Second-Order Stationary Points. - Robert Hönig, Javier Rando, Nicholas Carlini, Florian Tramèr:
Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI. - Michal Lukasik, Zhao Meng, Harikrishna Narasimhan, Yin-Wen Chang, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar:
Better autoregressive regression with LLMs via regression-aware fine-tuning. - Roman Worschech, Bernd Rosenow:
Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra. - Alex Nguyen, Gautam Reddy:
Differential learning kinetics govern the transition from memorization to generalization during in-context learning. - Mehdi Azabou, Krystal Xuejing Pan, Vinam Arora, Ian Jarratt Knight, Eva L. Dyer, Blake Aaron Richards:
Multi-session, multi-task neural decoding from distinct cell-types and brain regions. - Lucas Bandarkar, Benjamin Muller, Pritish Yuvraj, Rui Hou, Nayan Singhal, Hongjiang Lv, Bing Liu:
Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models. - Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison:
Bayesian Optimization via Continual Variational Last Layer Training. - Alan Nawzad Amin, Nate Gruver, Yilun Kuang, Yucen Lily Li, Hunter Elliott, Calvin McCarter, Aniruddh Raghu, Peyton Greenside, Andrew Gordon Wilson:
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences. - Ayesha Vermani, Josue Nassar, Hyungju Jeon, Matthew Dowling, Il Memming Park:
Meta-Dynamical State Space Models for Integrative Neural Data Analysis. - Xiaogeng Liu, Peiran Li, G. Edward Suh, Yevgeniy Vorobeychik, Zhuoqing Mao, Somesh Jha, Patrick McDaniel, Huan Sun, Bo Li, Chaowei Xiao:
AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs. - Kasia Kobalczyk, Mihaela van der Schaar:
Towards Automated Knowledge Integration From Human-Interpretable Representations. - Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S. Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan Ö. Arik, Danqi Chen, Tao Yu:
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval. - Josiah C. Kratz, Jacob Adamczyk:
Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics. - Pouya Hamadanian, Arash Nasr-Esfahany, Malte Schwarzkopf, Siddhartha Sen, Mohammad Alizadeh:
Online Reinforcement Learning in Non-Stationary Context-Driven Environments. - Song Wang, Peng Wang, Tong Zhou, Yushun Dong, Zhen Tan, Jundong Li:
CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models. - Chaoyi Zhu, Jiayi Tang, Jeroen M. Galjaard, Pin-Yu Chen, Robert Birke, Cornelis Bos, Lydia Y. Chen:
TabWak: A Watermark for Tabular Diffusion Models. - Kasia Kobalczyk, Nicolás Astorga, Tennison Liu, Mihaela van der Schaar:
Active Task Disambiguation with LLMs. - Ayush Kaushal, Tejas Vaidhya, Arnab Kumar Mondal, Tejas Pandey, Aaryan Bhagat, Irina Rish:
Surprising Effectiveness of pretraining Ternary Language Model at Scale. - Muhan Li, Lingji Kong, Sam Kriegman:
Generating Freeform Endoskeletal Robots. - Eshaan Nichani, Jason D. Lee, Alberto Bietti:
Understanding Factual Recall in Transformers via Associative Memories. - Kanan Gupta, Stephan Wojtowytsch:
Nesterov acceleration in benignly non-convex landscapes. - Jian Gao, Weidong Cao, Junyi Yang, Xuan Zhang:
AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies. - Han-Lin Hsieh, Maryam Shanechi:
Probabilistic Geometric Principal Component Analysis with application to neural data. - Zaid Khan, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal:
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback. - Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster:
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. - Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar:
Exact Certification of (Graph) Neural Networks Against Label Poisoning. - Ian Wu, Patrick Fernandes, Amanda Bertsch, Seungone Kim, Sina Khoshfetrat Pakazad, Graham Neubig:
Better Instruction-Following Through Minimum Bayes Risk. - Aritra Bhowmik, Pascal Mettes, Martin R. Oswald, Cees G. M. Snoek:
Union-over-Intersections: Object Detection beyond Winner-Takes-All. - Zhaoyang Li, Minghao Han, Xunyuan Yin:
MamKO: Mamba-based Koopman operator for modeling and predictive control. - Ziqi Lu, Heng Yang, Danfei Xu, Boyi Li, Boris Ivanovic, Marco Pavone, Yue Wang:
LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation models. - Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida, Chieh-Hsin Lai, Naoki Murata, Yuki Mitsufuji:
Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric. - Seth Aycock, David Stap, Di Wu, Christof Monz, Khalil Sima'an:
Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book? - Yushi Guan, Daniel Kwan, Jean Sebastien Dandurand, Xi Yan, Ruofan Liang, Yuxuan Zhang, Nilesh Jain, Nilesh A. Ahuja, Selvakumar Panneer, Nandita Vijaykumar:
Retri3D: 3D Neural Graphics Representation Retrieval. - Haobin Li, Peng Hu, Qianjun Zhang, Xi Peng, XitingLiu, Mouxing Yang:
Test-time Adaptation for Cross-modal Retrieval with Query Shift. - Tam Le, Jérôme Malick:
Universal generalization guarantees for Wasserstein distributionally robust models. - Jesse C. Cresswell, Bhargava Kumar, Yi Sui, Mouloud Belbahri:
Conformal Prediction Sets Can Cause Disparate Impact. - Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel:
In Search of Forgotten Domain Generalization. - Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann:
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning. - Gennadiy Averkov, Christopher Hojny, Maximilian Merkert:
On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth. - Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen:
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. - Haixin Zhong, Haoyu Wang, Wei P. Dai, Yuchao Huang, Mingyi Huang, Rubin Wang, Anna Wang Roe, Yuguo Yu:
Emergent Orientation Maps - - Mechanisms, Coding Efficiency and Robustness. - Seongmin Lee, Marcel Boehme:
How Much is Unseen Depends Chiefly on Information About the Seen. - Chi Zhang, Huaping Zhong, Kuan Zhang, Chengliang Chai, Rui Wang, Xinlin Zhuang, Tianyi Bai, Jiantao Qiu, Lei Cao, Ju Fan, Ye Yuan, Guoren Wang, Conghui He:
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models. - Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Ahmed Jellouli, Geovani Rizk, John Stephan:
Adaptive Gradient Clipping for Robust Federated Learning. - Marine Le Morvan, Gaël Varoquaux:
Imputation for prediction: beware of diminishing returns. - Hansi Yang, James T. Kwok:
Joint Gradient Balancing for Data Ordering in Finite-Sum Multi-Objective Optimization. - Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Michael Bloesch, Rishabh Joshi, Thomas Lampe, Junhyuk Oh, Nicolas Heess, Jonas Buchli, Martin A. Riedmiller:
Learning from negative feedback, or positive feedback or both. - Gideon Stein, Maha Shadaydeh, Jan Blunk, Niklas Penzel, Joachim Denzler:
CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series. - Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, Niki Kilbertus:
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes. - Yan Scholten, Stephan Günnemann:
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning. - Wenhong Zhu, Zhiwei He, Xiaofeng Wang, Pengfei Liu, Rui Wang:
Weak-to-Strong Preference Optimization: Stealing Reward from Weak Aligned Model. - Luca Pinchetti, Chang Qi, Oleh Lokshyn, Cornelius Emde, Amine M'Charrak, Mufeng Tang, Simon Frieder, Bayar Menzat, Gaspard Oliviers, Rafal Bogacz, Thomas Lukasiewicz, Tommaso Salvatori:
Benchmarking Predictive Coding Networks - Made Simple. - Minyoung Kim, Timothy M. Hospedales:
LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning. - Filippos Christianos, Georgios Papoudakis, Thomas Coste, Jianye Hao, Jun Wang, Kun Shao:
Lightweight Neural App Control. - Xu Han, Linghao Jin, Xiaofeng Liu, Paul Pu Liang:
Progressive Compositionality in Text-to-Image Generative Models. - Junqi Shi, Zhujia Chen, Hanfei Li, Qi Zhao, Ming Lu, Tong Chen, Zhan Ma:
On Quantizing Neural Representation for Variable-Rate Video Coding. - Aiwei Liu, Sheng Guan, Yiming Liu, Leyi Pan, Yifei Zhang, Liancheng Fang, Lijie Wen, Philip S. Yu, Xuming Hu:
Can Watermarked LLMs be Identified by Users via Crafted Prompts? - Xinyu He, Dongqi Fu, Hanghang Tong, Ross Maciejewski, Jingrui He:
Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency. - Ashok Vardhan Makkuva, Marco Bondaschi, Adway Girish, Alliot Nagle, Martin Jaggi, Hyeji Kim, Michael Gastpar:
Attention with Markov: A Curious Case of Single-layer Transformers. - Federico Adolfi, Martina G. Vilas, Todd Wareham:
The Computational Complexity of Circuit Discovery for Inner Interpretability. - Maosheng Yang:
Topological Schrödinger Bridge Matching. - Benjamin Frederick Spector, Simran Arora, Aaryan Singhal, Arjun Parthasarathy, Daniel Y. Fu, Christopher Ré:
ThunderKittens: Simple, Fast, and Adorable Kernels. - Chaitanya K. Joshi, Arian Rokkum Jamasb, Ramón Viñas Torné, Charles Harris, Simon V. Mathis, Alex Morehead, Rishabh Anand, Pietro Lio:
gRNAde: Geometric Deep Learning for 3D RNA inverse design. - Angelo Porrello, Lorenzo Bonicelli, Pietro Buzzega, Monica Millunzi, Simone Calderara, Rita Cucchiara:
A Second-Order Perspective on Model Compositionality and Incremental Learning. - Haohan Lin, Zhiqing Sun, Sean Welleck, Yiming Yang:
Lean-STaR: Learning to Interleave Thinking and Proving. - Haijin Zeng, Benteng Sun, Yongyong Chen, Jingyong Su, Yong Xu:
Spectral Compressive Imaging via Unmixing-driven Subspace Diffusion Refinement. - Xin Ding, Xiaoyu Liu, Zhijun Tu, Yun Zhang, Wei Li, Jie Hu, Hanting Chen, Yehui Tang, Zhiwei Xiong, Baoqun Yin, Yunhe Wang:
CBQ: Cross-Block Quantization for Large Language Models. - Yaowen Ye, Cassidy Laidlaw, Jacob Steinhardt:
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision. - Yu Chen, Jiatai Huang, Yan Dai, Longbo Huang:
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs. - Chen Chen, Daochang Liu, Mubarak Shah, Chang Xu:
Exploring Local Memorization in Diffusion Models via Bright Ending Attention. - Richard Zhuang, Tianhao Wu, Zhaojin Wen, Andrew Li, Jiantao Jiao, Kannan Ramchandran:
EmbedLLM: Learning Compact Representations of Large Language Models. - Hongkai Zheng, Wenda Chu, Bingliang Zhang, Zihui Wu, Austin Wang, Berthy Feng, Caifeng Zou, Yu Sun, Nikola Borislavov Kovachki, Zachary E. Ross, Katherine L. Bouman, Yisong Yue:
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences. - Weronika Ormaniec, Felix Dangel, Sidak Pal Singh:
What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis. - Rui Hu, Yifan Zhang, Zhuoran Li, Longbo Huang:
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks. - Shuo Xie, Mohamad Amin Mohamadi, Zhiyuan Li:
Adam Exploits ℓ∞-geometry of Loss Landscape via Coordinate-wise Adaptivity. - Haitao Lin, Guojiang Zhao, Odin Zhang, Yufei Huang, Lirong Wu, Cheng Tan, Zicheng Liu, Zhifeng Gao, Stan Z. Li:
CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph. - Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Qiaoyuan Yang, Yubo Zhang, Samson Zhou:
Fair Clustering in the Sliding Window Model. - Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping:
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models. - George Wang, Jesse Hoogland, Stan van Wingerden, Zach Furman, Daniel Murfet:
Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient. - Beichen Li, Rundi Wu, Armando Solar-Lezama, Changxi Zheng, Liang Shi, Bernd Bickel, Wojciech Matusik:
VLMaterial: Procedural Material Generation with Large Vision-Language Models. - Yukun Huang, Sanxing Chen, Hongyi Cai, Bhuwan Dhingra:
To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts. - Anh-Dung Dinh, Daochang Liu, Chang Xu:
Representative Guidance: Diffusion Model Sampling with Coherence. - Yi Zeng, Yu Yang, Andy Zhou, Jeffrey Ziwei Tan, Yuheng Tu, Yifan Mai, Kevin Klyman, Minzhou Pan, Ruoxi Jia, Dawn Song, Percy Liang, Bo Li:
AIR-BENCH 2024: A Safety Benchmark based on Regulation and Policies Specified Risk Categories. - Wenlong Deng, Yize Zhao, Vala Vakilian, Minghui Chen, Xiaoxiao Li, Christos Thrampoulidis:
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models. - Noga Mudrik, Ryan Ly, Oliver Rübel, Adam Shabti Charles:
CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations. - Daning Huang, Hanyang He, John Harlim, Yan Li:
Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels. - Minhyuk Seo, Hyunseo Koh, Jonghyun Choi:
Budgeted Online Continual Learning by Adaptive Layer Freezing and Frequency-based Sampling. - Han Qi, Haocheng Yin, Heng Yang:
Control-oriented Clustering of Visual Latent Representation. - Tiago da Silva, Rodrigo Barreto Alves, Eliezer de Souza da Silva, Amauri H. Souza, Vikas Garg, Samuel Kaski, Diego Mesquita:
When do GFlowNets learn the right distribution? - Mintong Kang, Bo Li:
R2-Guard: Robust Reasoning Enabled LLM Guardrail via Knowledge-Enhanced Logical Reasoning. - Ziyu Lu, Wuwei Zhang, Trung Le, Hao Wang, Uygar Sümbül, Eric Todd Shea-Brown, Lu Mi:
NetFormer: An interpretable model for recovering dynamical connectivity in neuronal population dynamics. - Jinwoo Kim, Olga Zaghen, Ayhan Suleymanzade, Youngmin Ryou, Seunghoon Hong:
Revisiting Random Walks for Learning on Graphs. - Jinwei Yao, Kaiqi Chen, Kexun Zhang, Jiaxuan You, Binhang Yuan, Zeke Wang, Tao Lin:
DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference. - Yining Li, Peizhong Ju, Ness B. Shroff:
How to Find the Exact Pareto Front for Multi-Objective MDPs? - Puning Zhao, Jiafei Wu, Zhe Liu, Li Shen, Zhikun Zhang, Rongfei Fan, Le Sun, Qingming Li:
Enhancing Learning with Label Differential Privacy by Vector Approximation. - Yichao Liang, Nishanth Kumar, Hao Tang, Adrian Weller, Joshua B. Tenenbaum, Tom Silver, João F. Henriques, Kevin Ellis:
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning. - Ashish J. Khisti, MohammadReza Ebrahimi, Hassan Dbouk, Arash Behboodi, Roland Memisevic, Christos Louizos:
Multi-Draft Speculative Sampling: Canonical Decomposition and Theoretical Limits. - Tyna Eloundou, Alex Beutel, David G. Robinson, Keren Gu, Anna-Luisa Brakman, Pamela Mishkin, Meghan Shah, Johannes Heidecke, Lilian Weng, Adam Tauman Kalai:
First-Person Fairness in Chatbots. - Zeju Qiu, Weiyang Liu, Haiwen Feng, Zhen Liu, Tim Z. Xiao, Katherine M. Collins, Joshua B. Tenenbaum, Adrian Weller, Michael J. Black, Bernhard Schölkopf:
Can Large Language Models Understand Symbolic Graphics Programs? - Ziyang Wu, Tianjiao Ding, Yifu Lu, Druv Pai, Jingyuan Zhang, Weida Wang, Yaodong Yu, Yi Ma, Benjamin David Haeffele:
Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction. - Jonathan Yuyang Zhou, Yao Xie:
Nonlinear Sequence Embedding by Monotone Variational Inequality. - Haoran Xu, Kenton Murray, Philipp Koehn, Hieu Hoang, Akiko Eriguchi, Huda Khayrallah:
X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale. - Hsun-Yu Kuo, Yin-Hsiang Liao, Yu-Chieh Chao, Wei-Yun Ma, Pu-Jen Cheng:
Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification. - Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch, Jonas Seng, Devendra Singh Dhami, Kristian Kersting:
Systems with Switching Causal Relations: A Meta-Causal Perspective. - Yorai Shaoul, Itamar Mishani, Shivam Vats, Jiaoyang Li, Maxim Likhachev:
Multi-Robot Motion Planning with Diffusion Models. - Paolo Pellizzoni, Till Hendrik Schulz, Karsten M. Borgwardt:
Graph Neural Networks Can (Often) Count Substructures. - Ruixuan Liu, Zhiqi Bu:
Towards hyperparameter-free optimization with differential privacy. - Yiheng Xu, Dunjie Lu, Zhennan Shen, Junli Wang, Zekun Wang, Yuchen Mao, Caiming Xiong, Tao Yu:
AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials. - Michael T. Pearce, Thomas Dooms, Alice Rigg, José Oramas, Lee Sharkey:
Bilinear MLPs enable weight-based mechanistic interpretability. - Wilson Wu, Louis Jaburi, Jacob Drori, Jason Gross:
Towards a Unified and Verified Understanding of Group-Operation Networks. - Antonios Antoniadis, Marek Eliás, Adam Polak, Moritz Venzin:
Approximation algorithms for combinatorial optimization with predictions. - Jacopo Iollo, Christophe Heinkelé, Pierre Alliez, Florence Forbes:
Bayesian Experimental Design Via Contrastive Diffusions. - Zeinab Navidi, Jun Ma, Esteban Miglietta, Le Liu, Anne E. Carpenter, Beth A. Cimini, Benjamin Haibe-Kains, Bo Wang:
MorphoDiff: Cellular Morphology Painting with Diffusion Models. - Richard Bergna, Sergio Calvo-Ordoñez, Felix L. Opolka, Pietro Lio, José Miguel Hernández-Lobato:
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations. - Matteo Gallici, Mattie Fellows, Benjamin Ellis, Bartomeu Pou, Ivan Masmitja, Jakob Nicolaus Foerster, Mario Martin:
Simplifying Deep Temporal Difference Learning. - Marta Skreta, Lazar Atanackovic, Joey Bose, Alexander Tong, Kirill Neklyudov:
The Superposition of Diffusion Models Using the Itô Density Estimator. - Zijie Geng, Jie Wang, Xijun Li, Fangzhou Zhu, Jianye Hao, Bin Li, Feng Wu:
Differentiable Integer Linear Programming. - Amit Chakrabarti, Jeffrey Jiang, David P. Woodruff, Taisuke Yasuda:
Streaming Algorithms For ℓp Flows and ℓp Regression. - Tao Lin, Yiling Chen:
Generalized Principal-Agent Problem with a Learning Agent. - Boyang Zheng, Chumeng Liang, Xiaoyu Wu:
Targeted Attack Improves Protection against Unauthorized Diffusion Customization. - Muhammed Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga, Marco Mondelli, Samet Oymak:
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws. - Hikaru Shindo, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting:
BlendRL: A Framework for Merging Symbolic and Neural Policy Learning. - Mengqi Zhang, Xiaotian Ye, Qiang Liu, Shu Wu, Pengjie Ren, Zhumin Chen:
Uncovering Overfitting in Large Language Model Editing. - Shiping Gao, Fanqi Wan, Jiajian Guo, Xiaojun Quan, Qifan Wang:
Advantage-Guided Distillation for Preference Alignment in Small Language Models. - Zhenyu Yang, Yuhang Hu, Zemin Du, Dizhan Xue, Shengsheng Qian, Jiahong Wu, Fan Yang, Weiming Dong, Changsheng Xu:
SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding. - Simiao Li, Yun Zhang, Wei Li, Hanting Chen, Wenjia Wang, Bingyi Jing, Shaohui Lin, Jie Hu:
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution. - Pau Rodríguez, Arno Blaas, Michal Klein, Luca Zappella, Nicholas Apostoloff, Marco Cuturi, Xavier Suau:
Controlling Language and Diffusion Models by Transporting Activations. - Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
Efficient and Accurate Explanation Estimation with Distribution Compression. - Dongping Chen, Ruoxi Chen, Shu Pu, Zhaoyi Liu, Yanru Wu, Caixi Chen, Benlin Liu, Yue Huang, Yao Wan, Pan Zhou, Ranjay Krishna:
Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment. - Yinuo Wang, Wenxuan Wang, Xujie Song, Tong Liu, Yuming Yin, Liangfa Chen, Likun Wang, Jingliang Duan, Shengbo Eben Li:
ODE-based Smoothing Neural Network for Reinforcement Learning Tasks. - Tal Wagner:
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities. - Tianfang Zhu, Dongli Hu, Jiandong Zhou, Kai Du, Anan Li:
Biologically Constrained Barrel Cortex Model Integrates Whisker Inputs and Replicates Key Brain Network Dynamics. - Zhiting Fan, Ruizhe Chen, Tianxiang Hu, Zuozhu Liu:
FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs. - Qingyun Li, Zhe Chen, Weiyun Wang, Wenhai Wang, Shenglong Ye, Zhenjiang Jin, Guanzhou Chen, Yinan He, Zhangwei Gao, Erfei Cui, Jiashuo Yu, Hao Tian, Jiasheng Zhou, Chao Xu, Bin Wang, Xingjian Wei, Wei Li, Wenjian Zhang, Bo Zhang, Pinlong Cai, et al.:
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text. - Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann:
MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds. - Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space. - Jingxuan Chen, Derek Yuen, Bin Xie, Yuhao Yang, Gongwei Chen, Zhihao Wu, Li Yixing, Xurui Zhou, Weiwen Liu, Shuai Wang, Kaiwen Zhou, Rui Shao, Liqiang Nie, Yasheng Wang, Jianye Hao, Jun Wang, Kun Shao:
Spa-Bench: a comprehensive Benchmark for Smartphone Agent Evaluation. - Yi Liu, Changran Xu, Yunhao Zhou, Zeju Li, Qiang Xu:
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model. - Qiqiang Lin, Muning Wen, Qiuying Peng, Guanyu Nie, Junwei Liao, Jun Wang, Xiaoyun Mo, Jiamu Zhou, Cheng Cheng, Yin Zhao, Jun Wang, Weinan Zhang:
Robust Function-Calling for On-Device Language Model via Function Masking. - Jiawen Qin, Haonan Yuan, Qingyun Sun, Lyujin Xu, Jiaqi Yuan, Pengfeng Huang, Zhaonan Wang, Xingcheng Fu, Hao Peng, Jianxin Li, Philip S. Yu:
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning. - Nicholas Gao, Eike Eberhard, Stephan Günnemann:
Learning Equivariant Non-Local Electron Density Functionals. - Shangyu Chen, Zizheng Pan, Jianfei Cai, Dinh Q. Phung:
PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction. - Qixin Zhang, Zongqi Wan, Yu Yang, Li Shen, Dacheng Tao:
Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency. - Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
PETRA: Parallel End-to-end Training with Reversible Architectures. - Lukas Rauch, Raphael Schwinger, Moritz Wirth, René Heinrich, Denis Huseljic, Marek Herde, Jonas Lange, Stefan Kahl, Bernhard Sick, Sven Tomforde, Christoph Scholz:
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics. - Lu Dai, Yijie Xu, Jinhui Ye, Hao Liu, Hui Xiong:
SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction. - Zeyu Li, Hongkun Dou, Shen Fang, Wang Han, Yue Deng, Lijun Yang:
Physics-aligned field reconstruction with diffusion bridge. - Qian Liu, Xiaosen Zheng, Niklas Muennighoff, Guangtao Zeng, Longxu Dou, Tianyu Pang, Jing Jiang, Min Lin:
RegMix: Data Mixture as Regression for Language Model Pre-training. - Xiangming Gu, Tianyu Pang, Chao Du, Qian Liu, Fengzhuo Zhang, Cunxiao Du, Ye Wang, Min Lin:
When Attention Sink Emerges in Language Models: An Empirical View. - Qijun Gan, Song Wang, Shengtao Wu, Jianke Zhu:
PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance. - Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling:
The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions. - Jan Betley, Xuchan Bao, Martín Soto, Anna Sztyber-Betley, James Chua, Owain Evans:
Tell me about yourself: LLMs are aware of their learned behaviors. - Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum:
COPER: Correlation-based Permutations for Multi-View Clustering. - Rei Higuchi, Pierre-Louis Poirion, Akiko Takeda:
Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization. - Olivia Wiles, Chuhan Zhang, Isabela Albuquerque, Ivana Kajic, Su Wang, Emanuele Bugliarello, Yasumasa Onoe, Pinelopi Papalampidi, Ira Ktena, Christopher Knutsen, Cyrus Rashtchian, Anant Nawalgaria, Jordi Pont-Tuset, Aida Nematzadeh:
Revisiting text-to-image evaluation with Gecko: on metrics, prompts, and human rating. - Yeongmin Kim, Kwanghyeon Lee, Minsang Park, Byeonghu Na, Il-Chul Moon:
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning. - Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein:
Bundle Neural Network for message diffusion on graphs. - Miruna T. Cretu, Charles Harris, Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Bruno E. Correia, Julien Roy, Emmanuel Bengio, Pietro Lio:
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints. - Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Yuri Prince, Björn Deiseroth, Andrés Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr:
u-μP: The Unit-Scaled Maximal Update Parametrization. - Gen Li, Yuchen Jiao:
Improved Convergence Rate for Diffusion Probabilistic Models. - Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, Yang Zhou, Jianyu Zhang, Niklas Nolte, Yuandong Tian, Matthijs Douze, Léon Bottou, Zhihao Jia, Beidi Chen:
MagicPIG: LSH Sampling for Efficient LLM Generation. - Xiaodong Chen, Yuxuan Hu, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen:
Streamlining Redundant Layers to Compress Large Language Models. - Qi Liu, Xinhao Zheng, Xudong Lu, Qinxiang Cao, Junchi Yan:
Rethinking and Improving Autoformalization: Towards a Faithful Metric and a Dependency Retrieval-based Approach. - Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel:
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. - Valerii Iakovlev, Harri Lähdesmäki:
Learning Spatiotemporal Dynamical Systems from Point Process Observations. - Zhanfeng Mo, Haosen Shi, Sinno Jialin Pan:
Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation. - Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu:
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models. - Erik Jones, Arjun Patrawala, Jacob Steinhardt:
Uncovering Gaps in How Humans and LLMs Interpret Subjective Language. - Zhanghao Zhouyin, Zixi Gan, Shishir Kumar Pandey, Linfeng Zhang, Qiangqiang Gu:
Learning local equivariant representations for quantum operators. - Bocheng Zeng, Qi Wang, Mengtao Yan, Yang Liu, Ruizhi Chengze, Yi Zhang, Hongsheng Liu, Zidong Wang, Hao Sun:
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems. - Thieu N. Vo, Duy-Tung Pham, Xin T. Tong, Tan Minh Nguyen:
Demystifying the Token Dynamics of Deep Selective State Space Models. - Yan Ru Pei:
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions. - Jinjie Ni, Yifan Song, Deepanway Ghosal, Bo Li, David Junhao Zhang, Xiang Yue, Fuzhao Xue, Yuntian Deng, Zian Zheng, Kaichen Zhang, Mahir Shah, Kabir Jain, Yang You, Michael Shieh:
MixEval-X: Any-to-any Evaluations from Real-world Data Mixture. - Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao:
Knowledge Localization: Mission Not Accomplished? Enter Query Localization! - Guibin Zhang, Xiangguo Sun, Yanwei Yue, Chonghe Jiang, Kun Wang, Tianlong Chen, Shirui Pan:
Graph Sparsification via Mixture of Graphs. - Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Realistic Evaluation of Deep Partial-Label Learning Algorithms. - Haofei Lu, Zhe Wu, Junliang Xing, Jianshu Li, Ruoyu Li, Zhe Li, Yuanchun Shi:
BodyGen: Advancing Towards Efficient Embodiment Co-Design. - Hengzhe Zhang, Qi Chen, Bing Xue, Wolfgang Banzhaf, Mengjie Zhang:
RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression. - Hongbo Li, Sen Lin, Lingjie Duan, Yingbin Liang, Ness B. Shroff:
Theory on Mixture-of-Experts in Continual Learning. - Shahryar Zehtabi, Dong-Jun Han, Rohit Parasnis, Seyyedali Hosseinalipour, Christopher G. Brinton:
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees. - Claas Voelcker, Marcel Hussing, Eric Eaton, Amir-massoud Farahmand, Igor Gilitschenski:
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL. - Ryuichi Kanoh, Mahito Sugiyama:
Linear Mode Connectivity in Differentiable Tree Ensembles. - Keda Tao, Jinjin Gu, Yulun Zhang, Xiucheng Wang, Nan Cheng:
Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model. - Run Luo, Yunshui Li, Longze Chen, Wanwei He, Ting-En Lin, Ziqiang Liu, Lei Zhang, Zikai Song, Hamid Rokny, Xiaobo Xia, Tongliang Liu, Binyuan Hui, Min Yang:
DEEM: Diffusion models serve as the eyes of large language models for image perception. - Eliot Xing, Vernon Luk, Jean Oh:
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation. - Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens, Hans Hallez:
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification. - Changmin Yu, Maneesh Sahani, Máté Lengyel:
Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA. - Zachary Novack, Ge Zhu, Jonah Casebeer, Julian J. McAuley, Taylor Berg-Kirkpatrick, Nicholas J. Bryan:
Presto! Distilling Steps and Layers for Accelerating Music Generation. - Yunhao Luo, Yilun Du:
Grounding Video Models to Actions through Goal Conditioned Exploration. - Ann-Kathrin Schuetz, A. W. P. Poon, Aobo Li:
RESuM: A Rare Event Surrogate Model for Physics Detector Design. - Wenxuan Zhang, Philip Torr, Mohamed Elhoseiny, Adel Bibi:
Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models. - Yunyang Li, Zaishuo Xia, Lin Huang, Xinran Wei, Samuel Harshe, Han Yang, Erpai Luo, Zun Wang, Jia Zhang, Chang Liu, Bin Shao, Mark Gerstein:
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems. - Xingyi Zhou, Anurag Arnab, Chen Sun, Cordelia Schmid:
Dense Video Object Captioning from Disjoint Supervision. - Yutong Chen, Marko Mihajlovic, Xiyi Chen, Yiming Wang, Sergey Prokudin, Siyu Tang:
SplatFormer: Point Transformer for Robust 3D Gaussian Splatting. - Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger:
DeLLMa: Decision Making Under Uncertainty with Large Language Models. - Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li Song, Yue Wang:
OmniRe: Omni Urban Scene Reconstruction. - Jan-Matthis Lueckmann, Alexander Immer, Alex Bo-Yuan Chen, Peter H. Li, Mariela D. Petkova, Nirmala A. Iyer, Luuk Willem Hesselink, Aparna Dev, Gudrun Ihrke, Woohyun Park, Alyson Petruncio, Aubrey Weigel, Wyatt Korff, Florian Engert, Jeff Lichtman, Misha B. Ahrens, Michal Januszewski, Viren Jain:
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish. - Peng Gao, Le Zhuo, Dongyang Liu, Ruoyi Du, Xu Luo, Longtian Qiu, Yuhang Zhang, Rongjie Huang, Shijie Geng, Renrui Zhang, Junlin Xie, Wenqi Shao, Zhengkai Jiang, Tianshuo Yang, Weicai Ye, Tong He, Jingwen He, Junjun He, Yu Qiao, Hongsheng Li:
Lumina-T2X: Scalable Flow-based Large Diffusion Transformer for Flexible Resolution Generation. - Hanlin Wu, Yuxuan Song, Jingjing Gong, Ziyao Cao, Yawen Ouyang, Jianbing Zhang, Hao Zhou, Wei-Ying Ma, Jingjing Liu:
A Periodic Bayesian Flow for Material Generation. - Hengrui Zhang, Liancheng Fang, Qitian Wu, Philip S. Yu:
DiffPuter: Empowering Diffusion Models for Missing Data Imputation. - Khai Nguyen, Hai Nguyen, Nhat Ho:
Towards Marginal Fairness Sliced Wasserstein Barycenter. - Donggoo Jung, Daehyun Kim, Tae Hyun Kim:
Continuous Exposure Learning for Low-light Image Enhancement using Neural ODEs. - Vincenzo Di Vito Francesco, Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto:
Learning to Solve Differential Equation Constrained Optimization Problems. - Luca Alessandro Silva, Barthélémy Meynard-Piganeau, Carlo Lucibello, Christoph Feinauer:
Fast Uncovering of Protein Sequence Diversity from Structure. - Chen Ma, Xinjie Xu, Shuyu Cheng, Qi Xuan:
Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors. - Dewei Zhou, Ji Xie, Zongxin Yang, Yi Yang:
3DIS: Depth-Driven Decoupled Image Synthesis for Universal Multi-Instance Generation. - Elvis Dohmatob, Yunzhen Feng, Arjun Subramonian, Julia Kempe:
Strong Model Collapse. - Artem M. Vysogorets, Kartik Ahuja, Julia Kempe:
DRoP: Distributionally Robust Data Pruning. - Xingqun Qi, Yatian Wang, Hengyuan Zhang, Jiahao Pan, Wei Xue, Shanghang Zhang, Wenhan Luo, Qifeng Liu, Yike Guo:
Co3Gesture: Towards Coherent Concurrent Co-speech 3D Gesture Generation with Interactive Diffusion. - Luca Scofano, Alessio Sampieri, Tommaso Campari, Valentino Sacco, Indro Spinelli, Lamberto Ballan, Fabio Galasso:
Following the Human Thread in Social Navigation. - Ghada Sokar, Johan Samir Obando-Ceron, Aaron C. Courville, Hugo Larochelle, Pablo Samuel Castro:
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL. - Xiaoran Jiao, Weian Mao, Wengong Jin, Peiyuan Yang, Hao Chen, Chunhua Shen:
Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. - Junjie Chen, Xiangheng He, Yusuke Miyao, Danushka Bollegala:
Improving Unsupervised Constituency Parsing via Maximizing Semantic Information. - Nico Messikommer, Jiaxu Xing, Elie Aljalbout, Davide Scaramuzza:
Student-Informed Teacher Training. - Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang, Qiushi Sun, Chengyou Jia, Kanzhi Cheng, Zichen Ding, Liheng Chen, Paul Pu Liang, Yu Qiao:
OS-ATLAS: Foundation Action Model for Generalist GUI Agents. - Qingni Wang, Tiantian Geng, Zhiyuan Wang, Teng Wang, Bo Fu, Feng Zheng:
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models. - Myungseo Song, Jin-Woo Park, Jong-Seok Lee:
Exploring the Camera Bias of Person Re-identification. - Fangkai Jiao, Geyang Guo, Xingxing Zhang, Nancy F. Chen, Shafiq Joty, Furu Wei:
Preference Optimization for Reasoning with Pseudo Feedback. - Jeongsol Kim, Geon Yeong Park, Hyungjin Chung, Jong Chul Ye:
Regularization by Texts for Latent Diffusion Inverse Solvers. - Zhi Gao, Bofei Zhang, Pengxiang Li, Xiaojian Ma, Tao Yuan, Yue Fan, Yuwei Wu, Yunde Jia, Song-Chun Zhu, Qing Li:
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage. - Jiahai Feng, Stuart Russell, Jacob Steinhardt:
Monitoring Latent World States in Language Models with Propositional Probes. - Makoto Shing, Kou Misaki, Han Bao, Sho Yokoi, Takuya Akiba:
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models. - Maxime Burchi, Radu Timofte:
Learning Transformer-based World Models with Contrastive Predictive Coding. - Zhaochong An, Guolei Sun, Yun Liu, Runjia Li, Min Wu, Ming-Ming Cheng, Ender Konukoglu, Serge J. Belongie:
Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation. - S. Sakshi, Utkarsh Tyagi, Sonal Kumar, Ashish Seth, Ramaneswaran Selvakumar, Oriol Nieto, Ramani Duraiswami, Sreyan Ghosh, Dinesh Manocha:
MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark. - Daniel Herbst, Stefanie Jegelka:
Higher-Order Graphon Neural Networks: Approximation and Cut Distance. - Scott Fujimoto, Pierluca D'Oro, Amy Zhang, Yuandong Tian, Michael Rabbat:
Towards General-Purpose Model-Free Reinforcement Learning. - Millicent Li, Tongfei Chen, Benjamin Van Durme, Patrick Xia:
Multi-Field Adaptive Retrieval. - Maxim Fishman, Brian Chmiel, Ron Banner, Daniel Soudry:
Scaling FP8 training to trillion-token LLMs. - Laurin Lux, Alexander H. Berger, Alexander Weers, Nico Stucki, Daniel Rueckert, Ulrich Bauer, Johannes C. Paetzold:
Topograph: An Efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation. - Han Li, Shaohui Li, Wenrui Dai, Maida Cao, Nuowen Kan, Chenglin Li, Junni Zou, Hongkai Xiong:
On Disentangled Training for Nonlinear Transform in Learned Image Compression. - Marie-Charlotte Brandenburg, Moritz Leo Grillo, Christoph Hertrich:
Decomposition Polyhedra of Piecewise Linear Functions. - Christopher Musco, R. Teal Witter:
Provably Accurate Shapley Value Estimation via Leverage Score Sampling. - Xueru Wen, Jie Lou, Yaojie Lu, Hongyu Lin, XingYu, Xinyu Lu, Ben He, Xianpei Han, Debing Zhang, Le Sun:
Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree? - Xiaoming Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, Ming Jin:
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts. - Milad Sefidgaran, Abdellatif Zaidi, Piotr Krasnowski:
Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors. - Zhaolin Hu, Yixiao Zhou, Zhongan Wang, Xin Li, Weimin Yang, Hehe Fan, Yi Yang:
OSDA Agent: Leveraging Large Language Models for De Novo Design of Organic Structure Directing Agents. - Zhongxiang Sun, Xiaoxue Zang, Kai Zheng, Jun Xu, Xiao Zhang, Weijie Yu, Yang Song, Han Li:
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability. - Michal Bortkiewicz, Wladyslaw Palucki, Vivek Myers, Tadeusz Dziarmaga, Tomasz Arczewski, Lukasz Kucinski, Benjamin Eysenbach:
Accelerating Goal-Conditioned Reinforcement Learning Algorithms and Research. - Jianhao Huang, Zixuan Wang, Jason D. Lee:
Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought. - Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, Takuma Seno:
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning. - Annan Yu, Dongwei Lyu, Soon Hoe Lim, Michael W. Mahoney, N. Benjamin Erichson:
Tuning Frequency Bias of State Space Models. - Evan Z. Wang, Federico Cassano, Catherine Wu, Yunfeng Bai, William Song, Vaskar Nath, Ziwen Han, Sean M. Hendryx, Summer Yue, Hugh Zhang:
Planning in Natural Language Improves LLM Search for Code Generation. - Tristan Luca Saidi, Abigail Hickok, Andrew J. Blumberg:
Recovering Manifold Structure Using Ollivier Ricci Curvature. - Huanjian Zhou, Lingxiao Huang, Baoxiang Wang:
Improved Approximation Algorithms for k-Submodular Maximization via Multilinear Extension. - Matthew Dowling, Cristina Savin:
Nonlinear multiregion neural dynamics with parametric impulse response communication channels. - Shreyas Kapur, Erik Jenner, Stuart Russell:
Diffusion On Syntax Trees For Program Synthesis. - Ryan McKenna:
Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML. - Guanting Dong, Keming Lu, Chengpeng Li, Tingyu Xia, Bowen Yu, Chang Zhou, Jingren Zhou:
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models. - Blake Bordelon, Alexander B. Atanasov, Cengiz Pehlevan:
How Feature Learning Can Improve Neural Scaling Laws. - Suorong Yang, Peng Ye, Wanli Ouyang, Dongzhan Zhou, Furao Shen:
A CLIP-Powered Framework for Robust and Generalizable Data Selection. - Xinyu Zhang, Daolang Huang, Samuel Kaski, Julien Martinelli:
PABBO: Preferential Amortized Black-Box Optimization. - Sunwoo Kim, Minkyu Kim, Dongmin Park:
Test-time Alignment of Diffusion Models without Reward Over-optimization. - Yuchen Duan, Weiyun Wang, Zhe Chen, Xizhou Zhu, Lewei Lu, Tong Lu, Yu Qiao, Hongsheng Li, Jifeng Dai, Wenhai Wang:
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures. - Danny Wang, Ruihong Qiu, Guangdong Bai, Zi Huang:
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation. - Shuyang Jiang, Yusheng Liao, Ya Zhang, Yanfeng Wang, Yu Wang:
Fine-tuning with Reserved Majority for Noise Reduction. - Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, Chunyuan Li:
LLaVA-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models. - Liu Ziyin, Isaac L. Chuang, Tomer Galanti, Tomaso A. Poggio:
Formation of Representations in Neural Networks. - Yuxin Wang, Xiaomeng Zhu, Weimin Lyu, Saeed Hassanpour, Soroush Vosoughi:
ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentences. - Soham Deshmukh, Shuo Han, Rita Singh, Bhiksha Raj:
ADIFF: Explaining audio difference using natural language. - Shuangqi Li, Hieu Le, Jingyi Xu, Mathieu Salzmann:
Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds. - Zhengbo Wang, Jian Liang, Ran He, Zilei Wang, Tieniu Tan:
LoRA-Pro: Are Low-Rank Adapters Properly Optimized? - Zhongyi Shui, Jianpeng Zhang, Weiwei Cao, Sinuo Wang, Ruizhe Guo, Le Lu, Lin Yang, Xianghua Ye, Tingbo Liang, Qi Zhang, Ling Zhang:
Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding. - Pei Zhou, Ruizhe Liu, Qian Luo, Fan Wang, Yibing Song, Yanchao Yang:
AutoCGP: Closed-Loop Concept-Guided Policies from Unlabeled Demonstrations. - Chengan He, Xin Sun, Zhixin Shu, Fujun Luan, Sören Pirk, Jorge Alejandro Amador Herrera, Dominik Ludewig Michels, Tuanfeng Yang Wang, Meng Zhang, Holly E. Rushmeier, Yi Zhou:
Perm: A Parametric Representation for Multi-Style 3D Hair Modeling. - Shifeng Xu, Yanzhu Liu, Adams Wai-Kin Kong:
Easing Training Process of Rectified Flow Models Via Lengthening Inter-Path Distance. - Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng, Shenhao Wang, Jinhua Zhao:
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks. - Hyuna Cho, Ziquan Wei, Seungjoo Lee, Tingting Dan, Guorong Wu, Won Hwa Kim:
Conditional Diffusion with Ordinal Regression: Longitudinal Data Generation for Neurodegenerative Disease Studies. - Daniel Ward, Mark Beaumont, Matteo Fasiolo:
SoftCVI: Contrastive variational inference with self-generated soft labels. - Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, Ming-Hsuan Yang:
MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion. - Ziyu Tang, Weicai Ye, Yifan Wang, Di Huang, Hujun Bao, Tong He, Guofeng Zhang:
ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction. - Yining Hong, Beide Liu, Maxine Wu, Yuanhao Zhai, Kai-Wei Chang, Linjie Li, Kevin Lin, Chung-Ching Lin, Jianfeng Wang, Zhengyuan Yang, Ying Nian Wu, Lijuan Wang:
SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation. - Haitao Yang, Yuan Dong, Hanwen Jiang, Dejia Xu, Georgios Pavlakos, Qixing Huang:
Atlas Gaussians Diffusion for 3D Generation. - Jingyang Zhang, Jingwei Sun, Eric C. Yeats, Yang Ouyang, Martin Kuo, Jianyi Zhang, Hao Frank Yang, Hai Li:
Min-K%++: Improved Baseline for Pre-Training Data Detection from Large Language Models. - Renjie Li, Panwang Pan, Bangbang Yang, Dejia Xu, Shijie Zhou, Xuanyang Zhang, Zeming Li, Achuta Kadambi, Zhangyang Wang, Zhengzhong Tu, Zhiwen Fan:
4K4DGen: Panoramic 4D Generation at 4K Resolution. - Mattia Segù, Luigi Piccinelli, Siyuan Li, Yung-Hsu Yang, Luc Van Gool, Bernt Schiele:
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking. - Bruce W. Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre L. Dognin, Manish Nagireddy, Amit Dhurandhar:
Programming Refusal with Conditional Activation Steering. - Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, Yilin Zhao, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, Bryan Catanzaro, Andrew Tao, Jan Kautz, Zhiding Yu, Guilin Liu:
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders. - Cong Chen, Mingyu Liu, Chenchen Jing, Yizhou Zhou, Fengyun Rao, Hao Chen, Bo Zhang, Chunhua Shen:
PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training. - Guy Tevet, Sigal Raab, Setareh Cohan, Daniele Reda, Zhengyi Luo, Xue Bin Peng, Amit Haim Bermano, Michiel van de Panne:
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control. - Vahideh Sanjaroonpouri, Pouria Ramazi:
Linear SCM Identification in the Presence of Confounders and Gaussian Noise. - Sajad Movahedi, Antonio Orvieto, Seyed-Mohsen Moosavi-Dezfooli:
Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture. - Onkar Kishor Susladkar, Jishu Sen Gupta, Chirag Sehgal, Sparsh Mittal, Rekha Singhal:
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion. - Dongmin Park, Sebin Kim, Taehong Moon, Minkyu Kim, Kangwook Lee, Jaewoong Cho:
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance. - Yuta Saito, Jihan Yao, Thorsten Joachims:
POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition. - Wayne Wu, Honglin He, Jack He, Yiran Wang, Chenda Duan, Zhizheng Liu, Quanyi Li, Bolei Zhou:
MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility. - Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu:
D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement. - Kaifeng Zhao, Gen Li, Siyu Tang:
DartControl: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control. - Sascha Marton, Tim Grams, Florian Vogt, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization. - Zhanpeng Zhou, Mingze Wang, Yuchen Mao, Bingrui Li, Junchi Yan:
Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late In Training. - Haofei Lu, Dongqi Han, Yifei Shen, Dongsheng Li:
What Makes a Good Diffusion Planner for Decision Making? - Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xiong, Doyen Sahoo:
ThinK: Thinner Key Cache by Query-Driven Pruning. - Xinran Liu, Yikun Bai, Rocio Diaz Martin, Kaiwen Shi, Ashkan Shahbazi, Bennett Allan Landman, Catie Chang, Soheil Kolouri:
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data. - Tianyuan Zhang, Zhengfei Kuang, Haian Jin, Zexiang Xu, Sai Bi, Hao Tan, He Zhang, Yiwei Hu, Milos Hasan, William T. Freeman, Kai Zhang, Fujun Luan:
RelitLRM: Generative Relightable Radiance for Large Reconstruction Models. - Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu, Rasa Hosseinzadeh, Zhaoyan Liu, George Stein, Jesse C. Cresswell, Gabriel Loaiza-Ganem:
A Geometric Framework for Understanding Memorization in Generative Models. - Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen:
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control. - Qiang Liu, Mengyu Chu, Nils Thuerey:
ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks. - Yi Zhang, Siwei Wang, Jiyuan Liu, Shengju Yu, Zhibin Dong, Suyuan Liu, Xinwang Liu, En Zhu:
DLEFT-MKC: Dynamic Late Fusion Multiple Kernel Clustering with Robust Tensor Learning via Min-Max Optimization. - Xin Dong, Yonggan Fu, Shizhe Diao, Wonmin Byeon, Zijia Chen, Ameya Sunil Mahabaleshwarkar, Shih-Yang Liu, Matthijs Van Keirsbilck, Min-Hung Chen, Yoshi Suhara, Yingyan Celine Lin, Jan Kautz, Pavlo Molchanov:
Hymba: A Hybrid-head Architecture for Small Language Models. - Varun Mulchandani, Jung-Eun Kim:
Severing Spurious Correlations with Data Pruning. - Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, Federico Tombari:
CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation. - Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui Liu, Fernando Gonzalez Adauto, Francesco Ortu, András Strausz, Mrinmaya Sachan, Rada Mihalcea, Yejin Choi, Bernhard Schölkopf:
Language Model Alignment in Multilingual Trolley Problems. - Canfer Akbulut, Kevin Robinson, Maribeth Rauh, Isabela Albuquerque, Olivia Wiles, Laura Weidinger, Verena Rieser, Yana Hasson, Nahema Marchal, Iason Gabriel, William Isaac, Lisa Anne Hendricks:
Century: A Framework and Dataset for Evaluating Historical Contextualisation of Sensitive Images. - Yuxuan Yao, Han Wu, Mingyang Liu, Sichun Luo, Xiongwei Han, Jie Liu, Zhijiang Guo, Linqi Song:
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling. - Zimu Lu, Aojun Zhou, Ke Wang, Houxing Ren, Weikang Shi, Junting Pan, Mingjie Zhan, Hongsheng Li:
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code. - James Liu, Pragaash Ponnusamy, Tianle Cai, Han Guo, Yoon Kim, Ben Athiwaratkun:
Training-Free Activation Sparsity in Large Language Models. - Jiahao Lu, Yifan Zhang, Qiuhong Shen, Xinchao Wang, Shuicheng Yan:
Poison-splat: Computation Cost Attack on 3D Gaussian Splatting. - Zhong Zheng, Haochen Zhang, Lingzhou Xue:
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition. - Simla Burcu Harma, Ayan Chakraborty, Elizaveta Kostenok, Danila Mishin, Dongho Ha, Babak Falsafi, Martin Jaggi, Ming Liu, Yunho Oh, Suvinay Subramanian, Amir Yazdanbakhsh:
Effective Interplay between Sparsity and Quantization: From Theory to Practice. - Tao Liu, Kai Wang, Senmao Li, Joost van de Weijer, Fahad Shahbaz Khan, Shiqi Yang, Yaxing Wang, Jian Yang, Ming-Ming Cheng:
One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt. - Zihui Zhang, Yafei Yang, Hongtao Wen, Bo Yang:
GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision. - Lu Yi, Zhewei Wei:
Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier. - Shufan Shen, Zhaobo Qi, Junshu Sun, Qingming Huang, Qi Tian, Shuhui Wang:
Enhancing Pre-trained Representation Classifiability can Boost its Interpretability. - Haiyang Wang, Yue Fan, Muhammad Ferjad Naeem, Yongqin Xian, Jan Eric Lenssen, Liwei Wang, Federico Tombari, Bernt Schiele:
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters. - Shengju Yu, Zhibin Dong, Siwei Wang, Pei Zhang, Yi Zhang, Xinwang Liu, Naiyang Guan, Tiejun Li, Yiu-ming Cheung:
Simple yet Effective Incomplete Multi-view Clustering: Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction. - Kaizhe Hu, Zihang Rui, Yao He, Yuyao Liu, Pu Hua, Huazhe Xu:
Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion. - Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh-Agrawal, Sandeep Singh Sandha, Siddartha V. Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum:
LiveBench: A Challenging, Contamination-Limited LLM Benchmark. - Junyu Zhang, Daochang Liu, Eunbyung Park, Shichao Zhang, Chang Xu:
Anti-Exposure Bias in Diffusion Models. - Hengwei Bian, Lingdong Kong, Haozhe Xie, Liang Pan, Yu Qiao, Ziwei Liu:
DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes. - Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, Aduri Pavan, N. V. Vinodchandran:
Computational Explorations of Total Variation Distance. - Chunming He, Chengyu Fang, Yulun Zhang, Longxiang Tang, Jinfa Huang, Kai Li, Zhenhua Guo, Xiu Li, Sina Farsiu:
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model. - Ao Li, Wei Fang, Hongbo Zhao, Le Lu, Ge Yang, Minfeng Xu:
MaRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers. - Yijie Guo, Bingjie Tang, Iretiayo Akinola, Dieter Fox, Abhishek Gupta, Yashraj Narang:
SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks. - Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun-Yan Zhu, Song Han:
SVDQuant: Absorbing Outliers by Low-Rank Component for 4-Bit Diffusion Models. - Junzhe Zhu, Yuanchen Ju, Junyi Zhang, Muhan Wang, Zhecheng Yuan, Kaizhe Hu, Huazhe Xu:
DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo. - Laurent Condat, Arto Maranjyan, Peter Richtárik:
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. - Hantao Zhang, Yuhe Liu, Jiancheng Yang, Shouhong Wan, Xinyuan Wang, Wei Peng, Pascal Fua:
LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models. - Chenze Shao, Fandong Meng, Jie Zhou:
Beyond Next Token Prediction: Patch-Level Training for Large Language Models. - Junyan Ye, Baichuan Zhou, Zilong Huang, Junan Zhang, Tianyi Bai, Hengrui Kang, Jun He, Honglin Lin, Zihao Wang, Tong Wu, Zhizheng Wu, Yiping Chen, Dahua Lin, Conghui He, Weijia Li:
LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models. - Jiajie Li, Brian R. Quaranto, Chenhui Xu, Ishan Mishra, Ruiyang Qin, Dancheng Liu, Peter C. W. Kim, Jinjun Xiong:
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data. - Peiwen Sun, Sitong Cheng, Xiangtai Li, Zhen Ye, Huadai Liu, Honggang Zhang, Wei Xue, Yike Guo:
Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation. - Qing Wu, Chenhe Du, Xuanyu Tian, Jingyi Yu, Yuyao Zhang, Hongjiang Wei:
Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation. - Ruifeng Li, Mingqian Li, Wei Liu, Yuhua Zhou, Xiangxin Zhou, Yuan Yao, Qiang Zhang, Hongyang Chen:
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery. - Sepehr Dehdashtian, Gautam Sreekumar, Vishnu Boddeti:
OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes. - Suqin Yuan, Runqi Lin, Lei Feng, Bo Han, Tongliang Liu:
Instance-dependent Early Stopping.
Accept (Poster)
- Yuankai Luo, Xiao-Ming Wu, Hao Zhu:
Beyond Random Masking: When Dropout meets Graph Convolutional Networks. - Rodrigo González Laiz, Tobias Schmidt, Steffen Schneider:
Self-supervised contrastive learning performs non-linear system identification. - Hyesu Lim, Jinho Choi, Jaegul Choo, Steffen Schneider:
Sparse autoencoders reveal selective remapping of visual concepts during adaptation. - Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng, Bryan Kian Hsiang Low:
PIED: Physics-Informed Experimental Design for Inverse Problems. - Dayuan Fu, Keqing He, Yejie Wang, Wentao Hong, Zhuoma Gongque, Weihao Zeng, Wei Wang, Jingang Wang, Xunliang Cai, Weiran Xu:
AgentRefine: Enhancing Agent Generalization through Refinement Tuning. - Yury Gorishniy, Akim Kotelnikov, Artem Babenko:
TabM: Advancing tabular deep learning with parameter-efficient ensembling. - Xiangyu Wu, Feng Yu, Yang Yang, Qing-Guo Chen, Jianfeng Lu:
Multi-Label Test-Time Adaptation with Bound Entropy Minimization. - Renxi Wang, Xudong Han, Lei Ji, Shu Wang, Timothy Baldwin, Haonan Li:
ToolGen: Unified Tool Retrieval and Calling via Generation. - Danni Yuan, Mingda Zhang, Shaokui Wei, Li Liu, Baoyuan Wu:
Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks. - Haoxi Li, Xueyang Tang, Jie Zhang, Song Guo, Sikai Bai, Peiran Dong, Yue Yu:
Causally Motivated Sycophancy Mitigation for Large Language Models. - Manuel Glöckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke:
Compositional simulation-based inference for time series. - Ouns El Harzli, Bernardo Cuenca Grau:
Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks. - Qian Chen, Lei Li, Qian Li, Jianghua Wu, Akang Wang, Ruoyu Sun, Xiaodong Luo, Tsung-Hui Chang, Qingjiang Shi:
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach. - Hao Wang, Zhengnan Li, Haoxuan Li, Xu Chen, Mingming Gong, BinChen, Zhichao Chen:
Optimal Transport for Time Series Imputation. - James Burgess, Xiaohan Wang, Yuhui Zhang, Anita Rau, Alejandro Lozano, Lisa Dunlap, Trevor Darrell, Serena Yeung-Levy:
Video Action Differencing. - Aishwarya Jayagopal, Yanrong Zhang, Robert John Walsh, Tuan Zea Tan, Anand D. Jeyasekharan, Vaibhav Rajan:
GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment. - Zhiwei He, Zhaopeng Tu, Xing Wang, Xingyu Chen, Zhijie Wang, Jiahao Xu, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang:
RaSA: Rank-Sharing Low-Rank Adaptation. - Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang, Shengmin Jiang, Yuxiao Dong, Jie Tang:
Scaling Speech-Text Pre-training with Synthetic Interleaved Data. - Rong-Xi Tan, Ke Xue, Shen-Huan Lyu, Haopu Shang, Yao Wang, Yaoyuan Wang, Sheng Fu, Chao Qian:
Offline Model-Based Optimization by Learning to Rank. - Prateek Garg, Lokesh Nagalapatti, Sunita Sarawagi:
From Search to Sampling: Generative Models for Robust Algorithmic Recourse. - Arkaprava Majumdar, M. Anand Krishna, P. K. Srijith:
Neural Wave Equation for Irregularly Sampled Sequence Data. - The Viet Bui, Thanh Hong Nguyen, Tien Mai:
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization. - Vincent Plassier, Alexander Fishkov, Mohsen Guizani, Maxim Panov, Eric Moulines:
Probabilistic Conformal Prediction with Approximate Conditional Validity. - Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong:
Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding. - Lokesh Nagalapatti, Ashutosh Srivastava, Sunita Sarawagi, Amit Sharma:
Robust Root Cause Diagnosis using In-Distribution Interventions. - Fu Luo, Xi Lin, Yaoxin Wu, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang:
Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems. - Arhaan Ahmad, Tanay Vineet Tayal, Ashutosh Gupta, S. Akshay:
Sensitivity Verification for Additive Decision Tree Ensembles. - Zijing Shi, Meng Fang, Ling Chen:
Monte Carlo Planning with Large Language Model for Text-Based Game Agents. - Haotian Wu, Gongpu Chen, Deniz Gündüz:
Actions Speak Louder Than Words: Rate-Reward Trade-off in Markov Decision Processes. - Siavash Ameli, Siyuan Zhuang, Ion Stoica, Michael W. Mahoney:
A Statistical Framework for Ranking LLM-based Chatbots. - Sujoy Bhore, Devdan Dey, Satyam Singh:
Online epsilon Net & Piercing Set for Geometric Concepts. - Donglei Yu, Yang Zhao, Jie Zhu, Yangyifan Xu, Yu Zhou, Chengqing Zong:
SimulPL: Aligning Human Preferences in Simultaneous Machine Translation. - Lewis Hammond, Sam Adam-Day:
Neural Interactive Proofs. - Konstantinos Karatapanis, Vasilis Kontonis, Christos Tzamos:
Oracle efficient truncated statistics. - Taishi Nakamura, Takuya Akiba, Kazuki Fujii, Yusuke Oda, Rio Yokota, Jun Suzuki:
Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization. - Thibaud Gloaguen, Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Black-Box Detection of Language Model Watermarks. - Hao Di, Tong He, Haishan Ye, Yinghui Huang, Xiangyu Chang, Guang Dai, Ivor W. Tsang:
ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs. - Nikola Jovanovic, Robin Staab, Maximilian Baader, Martin T. Vechev:
Ward: Provable RAG Dataset Inference via LLM Watermarks. - Song Duong, Florian Le Bronnec, Alexandre Allauzen, Vincent Guigue, Alberto Lumbreras, Laure Soulier, Patrick Gallinari:
SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation. - Indradyumna Roy, Eeshaan Jain, Soumen Chakrabarti, Abir De:
Clique Number Estimation via Differentiable Functions of Adjacency Matrix Permutations. - Yuxuan Zhou, Xien Liu, Chen Ning, Xiao Zhang, Ji Wu:
Reliable and Diverse Evaluation of LLM Medical Knowledge Mastery. - Antonis Antoniades, Albert Örwall, Kexun Zhang, Yuxi Xie, Anirudh Goyal, William Yang Wang:
SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement. - Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Language Models are Advanced Anonymizers. - Shu Yu, Chaochao Lu:
ADAM: An Embodied Causal Agent in Open-World Environments. - Darius Muglich, Johannes Forkel, Elise van der Pol, Jakob Nicolaus Foerster:
Expected Return Symmetries. - Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang:
Beware of Calibration Data for Pruning Large Language Models. - Guoxiong Gao, Yutong Wang, Jiedong Jiang, Qi Gao, Zihan Qin, Tianyi Xu, Bin Dong:
Herald: A Natural Language Annotated Lean 4 Dataset. - Ziye Huang, Haoqi Yuan, Yuhui Fu, Zongqing Lu:
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping. - Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu:
DPLM-2: A Multimodal Diffusion Protein Language Model. - Hyungjoo Chae, Namyoung Kim, Kai Tzu-iunn Ong, Minju Gwak, Gwanwoo Song, Jihoon Kim, Sunghwan Kim, Dongha Lee, Jinyoung Yeo:
Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation.