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ORCID12th ICLR 2024: Vienna, Austria
- The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net 2024
Accept (oral)
- Yonatan Oren, Nicole Meister, Niladri S. Chatterji, Faisal Ladhak, Tatsunori Hashimoto:
Proving Test Set Contamination in Black-Box Language Models. - Yapei Chang, Kyle Lo, Tanya Goyal, Mohit Iyyer:
BooookScore: A systematic exploration of book-length summarization in the era of LLMs. - Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli, Stéphane Mallat:
Generalization in diffusion models arises from geometry-adaptive harmonic representations. - Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade:
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions. - Gautam Reddy:
The mechanistic basis of data dependence and abrupt learning in an in-context classification task. - Yang Song, Prafulla Dhariwal:
Improved Techniques for Training Consistency Models. - Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, Wieland Brendel:
Provable Compositional Generalization for Object-Centric Learning. - Ching Fang, Kim Stachenfeld:
Predictive auxiliary objectives in deep RL mimic learning in the brain. - Haoqi Yuan, Zhancun Mu, Feiyang Xie, Zongqing Lu:
Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning. - Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, Peter Henderson:
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! - Ido Amos, Jonathan Berant, Ankit Gupta:
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors. - Yixiao Li, Yifan Yu, Chen Liang, Nikos Karampatziakis, Pengcheng He, Weizhu Chen, Tuo Zhao:
LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models. - Miltiadis Kofinas, Boris Knyazev, Yan Zhang, Yunlu Chen, Gertjan J. Burghouts, Efstratios Gavves, Cees G. M. Snoek, David W. Zhang:
Graph Neural Networks for Learning Equivariant Representations of Neural Networks. - Zaishuo Xia, Han Yang, Binghui Wang, Jinyuan Jia:
GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations. - Hyungho Na, Yunkyeong Seo, Il-Chul Moon:
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning. - Yogesh Verma, Markus Heinonen, Vikas Garg:
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. - Hengrui Zhang, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Xiao Qin, Christos Faloutsos, Huzefa Rangwala, George Karypis:
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space. - Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, Xiang Ren:
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement. - Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, Liwei Wang:
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness. - Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, Jianfeng Gao:
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts. - Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hötzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi:
Protein Discovery with Discrete Walk-Jump Sampling. - Kensen Shi, Joey Hong, Yinlin Deng, Pengcheng Yin, Manzil Zaheer, Charles Sutton:
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis. - Yeming Wen, Swarat Chaudhuri:
Batched Low-Rank Adaptation of Foundation Models. - Atsushi Shimizu, Xiaoou Cheng, Christopher Musco, Jonathan Weare:
Improved Active Learning via Dependent Leverage Score Sampling. - Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, Jianfeng Gao:
Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs. - Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar:
One-shot Empirical Privacy Estimation for Federated Learning. - Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan:
SWE-bench: Can Language Models Resolve Real-world Github Issues? - Seyed-Iman Mirzadeh, Keivan Alizadeh-Vahid, Sachin Mehta, Carlo C. del Mundo, Oncel Tuzel, Golnoosh Samei, Mohammad Rastegari, Mehrdad Farajtabar:
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models. - Yiding Jiang, Christina Baek, J. Zico Kolter:
On the Joint Interaction of Models, Data, and Features. - Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson, Mark S. Squillante, Vishnu Jejjala, Yang-Hui He, Kugendran Naidoo, Vasileios Kalantzis, Lior Horesh:
Topological data analysis on noisy quantum computers. - Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi:
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. - Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu:
"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection. - Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Joshua M. Susskind, Shuangfei Zhai:
Generative Modeling with Phase Stochastic Bridge. - Zengwei Yao, Liyong Guo, Xiaoyu Yang, Wei Kang, Fangjun Kuang, Yifan Yang, Zengrui Jin, Long Lin, Daniel Povey:
Zipformer: A faster and better encoder for automatic speech recognition. - Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber:
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. - Kim-Celine Kahl, Carsten T. Lüth, Maximilian Zenk, Klaus H. Maier-Hein, Paul F. Jaeger:
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation. - Xudong Shen, Chao Du, Tianyu Pang, Min Lin, Yongkang Wong, Mohan S. Kankanhalli:
Finetuning Text-to-Image Diffusion Models for Fairness. - Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov:
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models. - Seohong Park, Oleh Rybkin, Sergey Levine:
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction. - Yichen Wu, Long-Kai Huang, Renzhen Wang, Deyu Meng, Ying Wei:
Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction. - Bo Zhao, Robert M. Gower, Robin Walters, Rose Yu:
Improving Convergence and Generalization Using Parameter Symmetries. - Ricky T. Q. Chen, Yaron Lipman:
Flow Matching on General Geometries. - Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull, Michael J. Black, Bernhard Schölkopf:
Ghost on the Shell: An Expressive Representation of General 3D Shapes. - Pablo Pernias, Dominic Rampas, Mats L. Richter, Christopher Pal, Marc Aubreville:
Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models. - Yuxuan Song, Jingjing Gong, Hao Zhou, Mingyue Zheng, Jingjing Liu, Wei-Ying Ma:
Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks. - Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie E. Everett, Alexander A. Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Small-scale proxies for large-scale Transformer training instabilities. - Pascal Chang, Jingwei Tang, Markus Gross, Vinicius C. Azevedo:
How I Warped Your Noise: a Temporally-Correlated Noise Prior for Diffusion Models. - Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski:
Vision Transformers Need Registers. - Sergei Solonets, Daniil Sinitsyn, Lukas von Stumberg, Nikita Araslanov, Daniel Cremers:
An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment. - Hyosoon Jang, Minsu Kim, Sungsoo Ahn:
Learning Energy Decompositions for Partial Inference in GFlowNets. - Ian Gemp, Luke Marris, Georgios Piliouras:
Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization. - Giorgio Mariani, Irene Tallini, Emilian Postolache, Michele Mancusi, Luca Cosmo, Emanuele Rodolà:
Multi-Source Diffusion Models for Simultaneous Music Generation and Separation. - Haiming Wang, Huajian Xin, Chuanyang Zheng, Zhengying Liu, Qingxing Cao, Yinya Huang, Jing Xiong, Han Shi, Enze Xie, Jian Yin, Zhenguo Li, Xiaodan Liang:
LEGO-Prover: Neural Theorem Proving with Growing Libraries. - Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Dieter Fox, Abhishek Gupta:
ASID: Active Exploration for System Identification in Robotic Manipulation. - Germain Kolossov, Andrea Montanari, Pulkit Tandon:
Towards a statistical theory of data selection under weak supervision. - Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar:
Mastering Memory Tasks with World Models. - Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines:
Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems. - Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer, Jason Weston, Mike Lewis:
Self-Alignment with Instruction Backtranslation. - Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Pack Kaelbling, Dale Schuurmans, Pieter Abbeel:
Learning Interactive Real-World Simulators. - Shuo He, Chaojie Wang, Guowu Yang, Lei Feng:
Candidate Label Set Pruning: A Data-centric Perspective for Deep Partial-label Learning. - Jonathan Richens, Tom Everitt:
Robust agents learn causal world models. - Jen-tse Huang, Wenxuan Wang, Eric John Li, Man Ho Lam, Shujie Ren, Youliang Yuan, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu:
On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs. - Jisu Nam, Gyuseong Lee, Sunwoo Kim, Hyeonsu Kim, Hyoungwon Cho, Seyeon Kim, Seungryong Kim:
Diffusion Model for Dense Matching. - Shashanka Venkataramanan, Mamshad Nayeem Rizve, João Carreira, Yuki M. Asano, Yannis Avrithis:
Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video. - Panagiotis Eustratiadis, Lukasz Dudziak, Da Li, Timothy M. Hospedales:
Neural Fine-Tuning Search for Few-Shot Learning. - Qiuhao Zeng, Changjian Shui, Long-Kai Huang, Peng Liu, Xi Chen, Charles Ling, Boyu Wang:
Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time. - Ruoyu Chen, Hua Zhang, Siyuan Liang, Jingzhi Li, Xiaochun Cao:
Less is More: Fewer Interpretable Region via Submodular Subset Selection. - Jason Y. Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan, Shubham Tulsiani:
Cameras as Rays: Pose Estimation via Ray Diffusion. - Jie Hu, Vishwaraj Doshi, Do Young Eun:
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks. - Yuxin Wen, Yuchen Liu, Chen Chen, Lingjuan Lyu:
Detecting, Explaining, and Mitigating Memorization in Diffusion Models. - Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka:
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. - Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, Jiaya Jia:
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models. - Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin:
Amortizing intractable inference in large language models. - Ahmad Faiz, Sotaro Kaneda, Ruhan Wang, Rita Chukwunyere Osi, Prateek Sharma, Fan Chen, Lei Jiang:
LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models. - Izzeddin Gur, Hiroki Furuta, Austin V. Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, Aleksandra Faust:
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis. - Zhantao Yang, Ruili Feng, Han Zhang, Yujun Shen, Kai Zhu, Lianghua Huang, Yifei Zhang, Yu Liu, Deli Zhao, Jingren Zhou, Fan Cheng:
Lipschitz Singularities in Diffusion Models. - Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt:
Interpreting CLIP's Image Representation via Text-Based Decomposition. - Yang He, Lingao Xiao, Joey Tianyi Zhou, Ivor W. Tsang:
Multisize Dataset Condensation. - Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, Gang Zeng:
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation. - Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, Hao Tan:
LRM: Large Reconstruction Model for Single Image to 3D. - Wenxuan Li, Alan L. Yuille, Zongwei Zhou:
How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks? - Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. - Yubo Zhuang, Xiaohui Chen, Yun Yang, Richard Y. Zhang:
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming. - André F. Cruz, Moritz Hardt:
Unprocessing Seven Years of Algorithmic Fairness. - Ziheng Qin, Kai Wang, Zangwei Zheng, Jianyang Gu, Xiangyu Peng, Zhaopan Xu, Daquan Zhou, Lei Shang, Baigui Sun, Xuansong Xie, Yang You:
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning. - Yijie Lin, Jie Zhang, Zhenyu Huang, Jia Liu, Zujie Wen, Xi Peng:
Multi-granularity Correspondence Learning from Long-term Noisy Videos.
Accept (spotlight)
- Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri:
SaNN: Simple Yet Powerful Simplicial-aware Neural Networks. - Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Beyond Memorization: Violating Privacy via Inference with Large Language Models. - Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin T. Vechev:
Controlled Text Generation via Language Model Arithmetic. - Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, Jia Chen:
Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision. - Juno Kim, Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki:
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems. - Suhan Shetty, Teng Xue, Sylvain Calinon:
Generalized Policy Iteration using Tensor Approximation for Hybrid Control. - Maksim Velikanov, Maxim Panov, Dmitry Yarotsky:
Generalization error of spectral algorithms. - Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui:
Debiased Collaborative Filtering with Kernel-Based Causal Balancing. - Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca D. Dragan:
The Effective Horizon Explains Deep RL Performance in Stochastic Environments. - Ainaz Eftekhar, Kuo-Hao Zeng, Jiafei Duan, Ali Farhadi, Aniruddha Kembhavi, Ranjay Krishna:
Selective Visual Representations Improve Convergence and Generalization for Embodied AI. - Rui Zheng, Wei Shen, Yuan Hua, Wenbin Lai, Shihan Dou, Yuhao Zhou, Zhiheng Xi, Xiao Wang, Haoran Huang, Tao Gui, Qi Zhang, Xuanjing Huang:
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning. - Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low:
PINNACLE: PINN Adaptive ColLocation and Experimental points selection. - Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar:
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. - Guodong Wang, Yunhong Wang, Xiuguo Bao, Di Huang:
Rotation Has Two Sides: Evaluating Data Augmentation for Deep One-class Classification. - Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li:
Realistic Evaluation of Semi-supervised Learning Algorithms in Open Environments. - Denizalp Goktas, Amy Greenwald, Sadie Zhao, Alec Koppel, Sumitra Ganesh:
Efficient Inverse Multiagent Learning. - Tianqi Du, Yifei Wang, Yisen Wang:
On the Role of Discrete Tokenization in Visual Representation Learning. - Athul Paul Jacob, Yikang Shen, Gabriele Farina, Jacob Andreas:
The Consensus Game: Language Model Generation via Equilibrium Search. - Jake Grigsby, Linxi Fan, Yuke Zhu:
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents. - Zhuqing Liu, Xin Zhang, Jia Liu, Zhengyuan Zhu, Songtao Lu:
PILOT: An $\mathcal{O}(1/K)$-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation. - Ted Moskovitz, Aaditya K. Singh, DJ Strouse, Tuomas Sandholm, Ruslan Salakhutdinov, Anca D. Dragan, Stephen Marcus McAleer:
Confronting Reward Model Overoptimization with Constrained RLHF. - Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures. - Jiayang Liu, Yiming Bu, Daniel Tso, Qinru Qiu:
Improved Efficiency Based on Learned Saccade and Continuous Scene Reconstruction From Foveated Visual Sampling. - Danny Halawi, Jean-Stanislas Denain, Jacob Steinhardt:
Overthinking the Truth: Understanding how Language Models Process False Demonstrations. - Ibraheem Muhammad Moosa, Rui Zhang, Wenpeng Yin:
MT-Ranker: Reference-free machine translation evaluation by inter-system ranking. - Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, Greg Durrett:
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning. - Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie:
Harnessing Density Ratios for Online Reinforcement Learning. - Hanqi Zhou, Robert Bamler, Charley M. Wu, Álvaro Tejero-Cantero:
Predictive, scalable and interpretable knowledge tracing on structured domains. - Irene Cannistraci, Luca Moschella, Marco Fumero, Valentino Maiorca, Emanuele Rodolà:
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication. - Sumeet Batra, Bryon Tjanaka, Matthew Christopher Fontaine, Aleksei Petrenko, Stefanos Nikolaidis, Gaurav S. Sukhatme:
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning. - Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis:
Memorization Capacity of Multi-Head Attention in Transformers. - Jack Merullo, Carsten Eickhoff, Ellie Pavlick:
Circuit Component Reuse Across Tasks in Transformer Language Models. - Henry Li, Ronen Basri, Yuval Kluger:
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps. - Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis:
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation. - Ali Shahin Shamsabadi, Gefei Tan, Tudor Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller:
Confidential-DPproof: Confidential Proof of Differentially Private Training. - Weijia Shi, Sewon Min, Maria Lomeli, Chunting Zhou, Margaret Li, Xi Victoria Lin, Noah A. Smith, Luke Zettlemoyer, Wen-tau Yih, Mike Lewis:
In-Context Pretraining: Language Modeling Beyond Document Boundaries. - Yanai Elazar, Akshita Bhagia, Ian Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Evan Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, Hannaneh Hajishirzi, Noah A. Smith, Jesse Dodge:
What's In My Big Data? - Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh:
On Diffusion Modeling for Anomaly Detection. - Arman Isajanyan, Artur Shatveryan, David Kocharian, Zhangyang Wang, Humphrey Shi:
Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community. - Aakash Lahoti, Stefani Karp, Ezra Winston, Aarti Singh, Yuanzhi Li:
Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs. - Lizhang Chen, Bo Liu, Kaizhao Liang, Qiang Liu:
Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts. - Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaïd Harchaoui:
Distributionally Robust Optimization with Bias and Variance Reduction. - Garrett Tanzer, Mirac Suzgun, Eline Visser, Dan Jurafsky, Luke Melas-Kyriazi:
A Benchmark for Learning to Translate a New Language from One Grammar Book. - Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng:
Improving Offline RL by Blending Heuristics. - Jeonghye Kim, Suyoung Lee, Woojun Kim, Youngchul Sung:
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making. - Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? - Lei Li, Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Ningyu Zhang, Hua Wu:
Tool-Augmented Reward Modeling. - Fan-Ming Luo, Tian Xu, Xingchen Cao, Yang Yu:
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning. - Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback. - Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum:
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning. - Seyed Amir Hossein Saberi, Amir Najafi, Alireza Heidari, Mohammad Hosein Movasaghinia, Abolfazl S. Motahari, Babak H. Khalaj:
Out-Of-Domain Unlabeled Data Improves Generalization. - Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, Danqi Chen:
Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation. - Jingyu Chen, Runlin Lei, Zhewei Wei:
PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters. - Shanqi Liu, Dong Xing, Pengjie Gu, Xinrun Wang, Bo An, Yong Liu:
Solving Homogeneous and Heterogeneous Cooperative Tasks with Greedy Sequential Execution. - Chongyi Zheng, Benjamin Eysenbach, Homer Rich Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine:
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data. - Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello:
Multi-View Causal Representation Learning with Partial Observability. - Sohan Patnaik, Heril Changwal, Milan Aggarwal, Sumit Bhatia, Yaman Kumar, Balaji Krishnamurthy:
CABINET: Content Relevance-based Noise Reduction for Table Question Answering. - Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang:
Safe RLHF: Safe Reinforcement Learning from Human Feedback. - Ruqi Bai, Saurabh Bagchi, David I. Inouye:
Benchmarking Algorithms for Federated Domain Generalization. - Aditya Bhatt, Daniel Palenicek, Boris Belousov, Max Argus, Artemij Amiranashvili, Thomas Brox, Jan Peters:
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity. - Xuefeng Liu, Takuma Yoneda, Rick Stevens, Matthew R. Walter, Yuxin Chen:
Blending Imitation and Reinforcement Learning for Robust Policy Improvement. - Zhengyao Jiang, Yingchen Xu, Nolan Wagener, Yicheng Luo, Michael Janner, Edward Grefenstette, Tim Rocktäschel, Yuandong Tian:
H-GAP: Humanoid Control with a Generalist Planner. - Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot:
Unlocking the Power of Representations in Long-term Novelty-based Exploration. - Hong Wang, Zhongkai Hao, Jie Wang, Zijie Geng, Zhen Wang, Bin Li, Feng Wu:
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling. - Yunhe Zhang, Yan Sun, Jinyu Cai, Jicong Fan:
Deep Orthogonal Hypersphere Compression for Anomaly Detection. - Chenjie Mao, Qiaosheng Zhang, Zhen Wang, Xuelong Li:
On the Role of General Function Approximation in Offline Reinforcement Learning. - Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui:
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps. - Pratik Patil, Daniel LeJeune:
Asymptotically Free Sketched Ridge Ensembles: Risks, Cross-Validation, and Tuning. - Shuai Fu, Xiequn Wang, Qiushi Huang, Yu Zhang:
Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models. - Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, Zhijiang Guo:
Towards Understanding Factual Knowledge of Large Language Models. - Chunsan Hong, Byunghee Cha, Tae-Hyun Oh:
CAS: A Probability-Based Approach for Universal Condition Alignment Score. - Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, Christoph Feichtenhofer:
Demystifying CLIP Data. - Huafeng Qin, Xin Jin, Yun Jiang, Mounîm A. El-Yacoubi, Xinbo Gao:
Adversarial AutoMixup. - Junmo Cho, Jaesik Yoon, Sungjin Ahn:
Spatially-Aware Transformers for Embodied Agents. - Yanwei Wang, Tsun-Hsuan Wang, Jiayuan Mao, Michael Hagenow, Julie Shah:
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations. - Xiangyu Liu, Chenghao Deng, Yanchao Sun, Yongyuan Liang, Furong Huang:
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies. - Pingzhi Li, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen:
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy. - Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar:
On Bias-Variance Alignment in Deep Models. - Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, Yu Rong:
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases. - Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Kumar Ravikumar:
Spectrally Transformed Kernel Regression. - Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, Shirui Pan:
Online GNN Evaluation Under Test-time Graph Distribution Shifts. - Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj:
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. - Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, Yuntian Deng:
WildChat: 1M ChatGPT Interaction Logs in the Wild. - Tsung-Wei Ke, Sangwoo Mo, Stella X. Yu:
Learning Hierarchical Image Segmentation For Recognition and By Recognition. - Erfan Shayegani, Yue Dong, Nael B. Abu-Ghazaleh:
Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models. - Kyungmin Lee, Kihyuk Sohn, Jinwoo Shin:
DreamFlow: High-quality text-to-3D generation by Approximating Probability Flow. - Brian DuSell, David Chiang:
Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns. - Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, Maarten Sap:
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents. - Edward S. Hu, James Springer, Oleh Rybkin, Dinesh Jayaraman:
Privileged Sensing Scaffolds Reinforcement Learning. - Dominik Schmidt, Minqi Jiang:
Learning to Act without Actions. - Tianjian Li, Haoran Xu, Philipp Koehn, Daniel Khashabi, Kenton Murray:
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models. - Matt Barnes, Matthew Abueg, Oliver F. Lange, Matt Deeds, Jason Trader, Denali Molitor, Markus Wulfmeier, Shawn O'Banion:
Massively Scalable Inverse Reinforcement Learning in Google Maps. - Yian Wang, Juntian Zheng, Zhehuan Chen, Zhou Xian, Gu Zhang, Chao Liu, Chuang Gan:
Thin-Shell Object Manipulations With Differentiable Physics Simulations. - Vaidehi Patil, Peter Hase, Mohit Bansal:
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks. - Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Neha Gupta, Sanjiv Kumar:
Learning to Reject Meets Long-tail Learning. - Katherine L. Hermann, Hossein Mobahi, Thomas Fel, Michael Curtis Mozer:
On the Foundations of Shortcut Learning. - Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards:
Synaptic Weight Distributions Depend on the Geometry of Plasticity. - Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas:
Graph Metanetworks for Processing Diverse Neural Architectures. - Peiran Yu, Junyi Li, Heng Huang:
Dropout Enhanced Bilevel Training. - Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta:
Privacy Amplification for Matrix Mechanisms. - Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu:
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation. - Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. - Noga Alon, Dmitrii Avdiukhin, Dor Elboim, Orr Fischer, Grigory Yaroslavtsev:
Optimal Sample Complexity of Contrastive Learning. - Wenlong Chen, Yegor Klochkov, Yang Liu:
Post-hoc bias scoring is optimal for fair classification. - Pengfei He, Han Xu, Jie Ren, Yingqian Cui, Shenglai Zeng, Hui Liu, Charu C. Aggarwal, Jiliang Tang:
Sharpness-Aware Data Poisoning Attack. - Maryam Haghighat, Peyman Moghadam, Shaheer Mohamed, Piotr Koniusz:
Pre-training with Random Orthogonal Projection Image Modeling. - Fabricio Arend Torres, Marcello Massimo Negri, Marco Inversi, Jonathan Aellen, Volker Roth:
Lagrangian Flow Networks for Conservation Laws. - Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau:
Linearity of Relation Decoding in Transformer Language Models. - Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel, Martin Trapp, Arno Solin, Nicolas Gillis, Antonio Vergari:
Subtractive Mixture Models via Squaring: Representation and Learning. - Jiawei Ge, Shange Tang, Jianqing Fan, Chi Jin:
On the Provable Advantage of Unsupervised Pretraining. - Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens:
TorchRL: A data-driven decision-making library for PyTorch. - Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang, Chongjie Zhang, Lei Han, Tong Zhang:
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. - James Harrison, John Willes, Jasper Snoek:
Variational Bayesian Last Layers. - Md Mofijul Islam, Alexi Gladstone, Riashat Islam, Tariq Iqbal:
EQA-MX: Embodied Question Answering using Multimodal Expression. - Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Xin Jiang, Qun Liu, Lei Chen:
Retrieval-based Disentangled Representation Learning with Natural Language Supervision. - Montgomery Bohde, Meng Liu, Alexandra Saxton, Shuiwang Ji:
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods. - Tom Sherborne, Naomi Saphra, Pradeep Dasigi, Hao Peng:
TRAM: Bridging Trust Regions and Sharpness Aware Minimization. - Olga Fourkioti, Mat De Vries, Chris Bakal:
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images. - Maximilian Seitzer, Sjoerd van Steenkiste, Thomas Kipf, Klaus Greff, Mehdi S. M. Sajjadi:
DyST: Towards Dynamic Neural Scene Representations on Real-World Videos. - Jie Hao, Xiaochuan Gong, Mingrui Liu:
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis. - Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel:
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation. - Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts:
DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines. - Wenhan Cao, Wei Pan:
Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control. - Advait Harshal Gadhikar, Rebekka Burkholz:
Masks, Signs, And Learning Rate Rewinding. - Zhan Zhuang, Yu Zhang, Ying Wei:
Gradual Domain Adaptation via Gradient Flow. - Jiarong Liu, Yifan Zhong, Siyi Hu, Haobo Fu, Qiang Fu, Xiaojun Chang, Yaodong Yang:
Maximum Entropy Heterogeneous-Agent Reinforcement Learning. - Junyi An, Chao Qu, Zhipeng Zhou, Fenglei Cao, Yinghui Xu, Yuan Qi, Furao Shen:
Hybrid Directional Graph Neural Network for Molecules. - Zhengmian Hu, Lichang Chen, Xidong Wu, Yihan Wu, Hongyang Zhang, Heng Huang:
Unbiased Watermark for Large Language Models. - Neehal Tumma, Mathias Lechner, Noel Loo, Ramin M. Hasani, Daniela Rus:
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control. - Size Wu, Wenwei Zhang, Lumin Xu, Sheng Jin, Xiangtai Li, Wentao Liu, Chen Change Loy:
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction. - Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu:
Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. - Yehui Tang, Hao Xiong, Nianzu Yang, Tailong Xiao, Junchi Yan:
Towards LLM4QPE: Unsupervised Pretraining of Quantum Property Estimation and A Benchmark. - Guikun Xu, Yongquan Jiang, PengChuan Lei, Yan Yang, Jim Chen:
GTMGC: Using Graph Transformer to Predict Molecule's Ground-State Conformation. - Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios Chrysos, Volkan Cevher:
Generalization of Scaled Deep ResNets in the Mean-Field Regime. - Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. - Christian Gumbsch, Noor Sajid, Georg Martius, Martin V. Butz:
Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics. - Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun:
Prediction without Preclusion: Recourse Verification with Reachable Sets. - Liyuan Mao, Haoran Xu, Weinan Zhang, Xianyuan Zhan:
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update. - Ziming Hong, Zhenyi Wang, Li Shen, Yu Yao, Zhuo Huang, Shiming Chen, Chuanwu Yang, Mingming Gong, Tongliang Liu:
Improving Non-Transferable Representation Learning by Harnessing Content and Style. - Donghao Luo, Xue Wang:
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis. - Yingtian Zou, Kenji Kawaguchi, Yingnan Liu, Jiashuo Liu, Mong-Li Lee, Wynne Hsu:
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness. - Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V. Chawla, Stan Z. Li:
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding. - Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. - Lijia Yu, Xiao-Shan Gao, Lijun Zhang:
Optimal robust Memorization with ReLU Neural Networks. - Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann, Leonel Rozo:
Neural Contractive Dynamical Systems. - Vivien Cabannes, Elvis Dohmatob, Alberto Bietti:
Scaling Laws for Associative Memories. - Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, Tao Yu:
Text2Reward: Reward Shaping with Language Models for Reinforcement Learning. - Alexander Theus, Olin Geimer, Friedrich Wicke, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Towards Meta-Pruning via Optimal Transport. - Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, Ping Luo, Ziwei Liu, Yali Wang, Limin Wang, Yu Qiao:
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation. - Suhwan Choi, Myeongho Jeon, Yeonjung Hwang, Jeonglyul Oh, Sungjun Lim, Joonseok Lee, Myungjoo Kang:
Dictionary Contrastive Learning for Efficient Local Supervision without Auxiliary Networks. - Yang Yang, Wenhai Wang, Zhe Chen, Jifeng Dai, Liang Zheng:
Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments. - Ce Ju, Reinmar J. Kobler, Liyao Tang, Cuntai Guan, Motoaki Kawanabe:
Deep Geodesic Canonical Correlation Analysis for Covariance-Based Neuroimaging Data. - Yameng Peng, Andy Song, Haytham M. Fayek, Vic Ciesielski, Xiaojun Chang:
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS. - Jiayuan Gu, Sean Kirmani, Paul Wohlhart, Yao Lu, Montserrat Gonzalez Arenas, Kanishka Rao, Wenhao Yu, Chuyuan Fu, Keerthana Gopalakrishnan, Zhuo Xu, Priya Sundaresan, Peng Xu, Hao Su, Karol Hausman, Chelsea Finn, Quan Vuong, Ted Xiao:
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches. - Kai Shen, Zeqian Ju, Xu Tan, Eric Liu, Yichong Leng, Lei He, Tao Qin, Sheng Zhao, Jiang Bian:
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers. - Manish Prajapat, Mojmir Mutny, Melanie N. Zeilinger, Andreas Krause:
Submodular Reinforcement Learning. - Jiahuan Yan, Bo Zheng, Hongxia Xu, Yiheng Zhu, Danny Z. Chen, Jimeng Sun, Jian Wu, Jintai Chen:
Making Pre-trained Language Models Great on Tabular Prediction. - Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen:
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. - Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song:
The False Promise of Imitating Proprietary Language Models. - Thomas T. C. K. Zhang, Leonardo Felipe Toso, James Anderson, Nikolai Matni:
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data. - Zhipeng Xie, Yahe Li:
Information Retention via Learning Supplemental Features. - Yuan Feng, Yukun Cao, Hairu Wang, Xike Xie, S. Kevin Zhou:
Mayfly: a Neural Data Structure for Graph Stream Summarization. - Hanyu Zhou, Yi Chang, Haoyue Liu, Wending Yan, Yuxing Duan, Zhiwei Shi, Luxin Yan:
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow. - Yijue Dai, Wenzhong Yan, Feng Yin:
Graphical Multioutput Gaussian Process with Attention. - Seunghan Lee, Taeyoung Park, Kibok Lee:
Soft Contrastive Learning for Time Series. - Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi:
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. - Hongbin Huang, Minghua Chen, Xiao Qiao:
Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns. - Yuchuan Tian, Hanting Chen, Xutao Wang, Zheyuan Bai, Qinghua Zhang, Ruifeng Li, Chao Xu, Yunhe Wang:
Multiscale Positive-Unlabeled Detection of AI-Generated Texts. - Shiqiang Wang, Mingyue Ji:
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging. - Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto:
Identifying the Risks of LM Agents with an LM-Emulated Sandbox. - Jiayi Wei, Greg Durrett, Isil Dillig:
Coeditor: Leveraging Repo-level Diffs for Code Auto-editing. - Zhijian Xu, Ailing Zeng, Qiang Xu:
FITS: Modeling Time Series with 10k Parameters. - Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu:
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. - Xiao Hu, Jianxiong Li, Xianyuan Zhan, Qing-Shan Jia, Ya-Qin Zhang:
Query-Policy Misalignment in Preference-Based Reinforcement Learning. - Joonhun Lee, Myeongho Jeon, Myungjoo Kang, Kyunghyun Park:
Feature-aligned N-BEATS with Sinkhorn divergence. - Taehyeon Kim, Joonkee Kim, Gihun Lee, Se-Young Yun:
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions. - Zixi Wei, Senlin Shu, Yuzhou Cao, Hongxin Wei, Bo An, Lei Feng:
Consistent Multi-Class Classification from Multiple Unlabeled Datasets. - Hongwei Ren, Yue Zhou, Xiaopeng Lin, Yulong Huang, Haotian Fu, Jie Song, Bojun Cheng:
SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition. - Shida Wang, Zhong Li, Qianxiao Li:
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks. - Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh, Kee-Eung Kim:
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies. - Yuchen Hu, Chen Chen, Chao-Han Huck Yang, Ruizhe Li, Chao Zhang, Pin-Yu Chen, Engsiong Chng:
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition. - Minyoung Park, Mirae Do, YeonJae Shin, Jaeseok Yoo, Jongkwang Hong, Joongrock Kim, Chul Lee:
H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields. - Ke Xue, Ren-Jian Wang, Pengyi Li, Dong Li, Jianye Hao, Chao Qian:
Sample-Efficient Quality-Diversity by Cooperative Coevolution. - Sewon Min, Suchin Gururangan, Eric Wallace, Weijia Shi, Hannaneh Hajishirzi, Noah A. Smith, Luke Zettlemoyer:
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore. - Hang Xu, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng:
Dynamic Discounted Counterfactual Regret Minimization. - Dante Everaert, Christopher Potts:
GIO: Gradient Information Optimization for Training Dataset Selection. - Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, Amir Barati Farimani:
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training. - Karsten Roth, Lukas Thede, A. Sophia Koepke, Oriol Vinyals, Olivier J. Hénaff, Zeynep Akata:
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model. - Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson:
Robustifying State-space Models for Long Sequences via Approximate Diagonalization. - Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Preference-Based Reinforcement Learning. - Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, Yejin Choi:
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory. - Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
Provable Reward-Agnostic Preference-Based Reinforcement Learning. - Qiyu Kang, Kai Zhao, Qinxu Ding, Feng Ji, Xuhao Li, Wenfei Liang, Yang Song, Wee Peng Tay:
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND. - S. Chandra Mouli, Muhammad Ashraful Alam, Bruno Ribeiro:
MetaPhysiCa: Improving OOD Robustness in Physics-informed Machine Learning. - Kimia Hamidieh, Haoran Zhang, Swami Sankaranarayanan, Marzyeh Ghassemi:
Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation. - Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn:
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. - Zihan Wang, Arthur Jacot:
Implicit bias of SGD in L2-regularized linear DNNs: One-way jumps from high to low rank. - Ziyu Wang, Lejun Min, Gus Xia:
Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models. - Jiuding Sun, Chantal Shaib, Byron C. Wallace:
Evaluating the Zero-shot Robustness of Instruction-tuned Language Models. - Michael Kleinman, Alessandro Achille, Stefano Soatto:
Critical Learning Periods Emerge Even in Deep Linear Networks. - Ethan Steinberg, Jason Alan Fries, Yizhe Xu, Nigam Shah:
MOTOR: A Time-to-Event Foundation Model For Structured Medical Records. - Lirui Wang, Yiyang Ling, Zhecheng Yuan, Mohit Shridhar, Chen Bao, Yuzhe Qin, Bailin Wang, Huazhe Xu, Xiaolong Wang:
GenSim: Generating Robotic Simulation Tasks via Large Language Models. - Runtian Zhai, Bingbin Liu, Andrej Risteski, J. Zico Kolter, Pradeep Kumar Ravikumar:
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. - Angelica Chen, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra:
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs. - Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian Fatras, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong:
SE(3)-Stochastic Flow Matching for Protein Backbone Generation. - Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng Li, Bo Li, Zhangyang Wang:
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer. - Marc Rußwurm, Konstantin Klemmer, Esther Rolf, Robin Zbinden, Devis Tuia:
Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks. - Carl Hvarfner, Frank Hutter, Luigi Nardi:
A General Framework for User-Guided Bayesian Optimization. - Yiheng Xu, Hongjin Su, Chen Xing, Boyu Mi, Qian Liu, Weijia Shi, Binyuan Hui, Fan Zhou, Yitao Liu, Tianbao Xie, Zhoujun Cheng, Siheng Zhao, Lingpeng Kong, Bailin Wang, Caiming Xiong, Tao Yu:
Lemur: Harmonizing Natural Language and Code for Language Agents. - Antoine Gonon, Nicolas Brisebarre, Elisa Riccietti, Rémi Gribonval:
A path-norm toolkit for modern networks: consequences, promises and challenges. - Jinyi Hu, Yuan Yao, Chongyi Wang, Shan Wang, Yinxu Pan, Qianyu Chen, Tianyu Yu, Hanghao Wu, Yue Zhao, Haoye Zhang, Xu Han, Yankai Lin, Jiao Xue, Dahai Li, Zhiyuan Liu, Maosong Sun:
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages. - Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Neil Houlsby:
From Sparse to Soft Mixtures of Experts. - Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan:
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives. - Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. - Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach:
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis. - Dan Haramati, Tal Daniel, Aviv Tamar:
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels. - Wei Yao, Chengming Yu, Shangzhi Zeng, Jin Zhang:
Constrained Bi-Level Optimization: Proximal Lagrangian Value Function Approach and Hessian-free Algorithm. - Joseph Early, Gavin K. C. Cheung, Kurt Cutajar, Hanting Xie, Jas Kandola, Niall Twomey:
Inherently Interpretable Time Series Classification via Multiple Instance Learning. - Christos Louizos, Matthias Reisser, Denis Korzhenkov:
A Mutual Information Perspective on Federated Contrastive Learning. - Yan Sun, Jicong Fan:
MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy. - Margalit Glasgow:
SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem. - Kaijie Zhu, Jiaao Chen, Jindong Wang, Neil Zhenqiang Gong, Diyi Yang, Xing Xie:
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks. - Tim Franzmeyer, Stephen Marcus McAleer, João F. Henriques, Jakob Nicolaus Foerster, Philip Torr, Adel Bibi, Christian Schröder de Witt:
Illusory Attacks: Information-theoretic detectability matters in adversarial attacks. - William Wei Wang, Dongqi Han, Xufang Luo, Dongsheng Li:
Addressing Signal Delay in Deep Reinforcement Learning. - Jiayan Teng, Wendi Zheng, Ming Ding, Wenyi Hong, Jianqiao Wangni, Zhuoyi Yang, Jie Tang:
Relay Diffusion: Unifying diffusion process across resolutions for image synthesis. - Yingqing He, Shaoshu Yang, Haoxin Chen, Xiaodong Cun, Menghan Xia, Yong Zhang, Xintao Wang, Ran He, Qifeng Chen, Ying Shan:
ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models. - Guowei Xu, Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Zhecheng Yuan, Tianying Ji, Yu Luo, Xiaoyu Liu, Jiaxin Yuan, Pu Hua, Shuzhen Li, Yanjie Ze, Hal Daumé III, Furong Huang, Huazhe Xu:
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization. - Nuoya Xiong, Lijun Ding, Simon Shaolei Du:
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization. - Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, Xuansong Xie:
AnyText: Multilingual Visual Text Generation and Editing. - Yingwei Ma, Yue Liu, Yue Yu, Yuanliang Zhang, Yu Jiang, Changjian Wang, Shanshan Li:
At Which Training Stage Does Code Data Help LLMs Reasoning? - Joo Chan Lee, Daniel Rho, Seungtae Nam, Jong Hwan Ko, Eunbyung Park:
Coordinate-Aware Modulation for Neural Fields. - Kaichao You, Guo Qin, Anchang Bao, Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long:
Efficient ConvBN Blocks for Transfer Learning and Beyond. - Ashmit Khandelwal, Aditya Agrawal, Aanisha Bhattacharyya, Yaman Kumar, Somesh Singh, Uttaran Bhattacharya, Ishita Dasgupta, Stefano Petrangeli, Rajiv Ratn Shah, Changyou Chen, Balaji Krishnamurthy:
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior. - Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, Yuxin Chen:
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints. - Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, Seungone Kim, Yongrae Jo, James Thorne, Juho Kim, Minjoon Seo:
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets. - Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang:
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset. - Yefei He, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang:
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models. - Qingqing Cao, Sewon Min, Yizhong Wang, Hannaneh Hajishirzi:
BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models. - Ziqi Pang, Ziyang Xie, Yunze Man, Yu-Xiong Wang:
Frozen Transformers in Language Models Are Effective Visual Encoder Layers. - Junyan Cheng, Peter Chin:
SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series. - Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh:
Learning Performance-Improving Code Edits. - Khai Nguyen, Nicola Bariletto, Nhat Ho:
Quasi-Monte Carlo for 3D Sliced Wasserstein. - Thien Le, Luana Ruiz, Stefanie Jegelka:
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs. - Yihan Du, R. Srikant, Wei Chen:
Cascading Reinforcement Learning. - Benjamin Lyo, Cristina Savin:
Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities. - Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber:
On the hardness of learning under symmetries. - Haochen Luo, Jindong Gu, Fengyuan Liu, Philip Torr:
An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models. - Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, Muhan Zhang:
One For All: Towards Training One Graph Model For All Classification Tasks. - Yining Jiao, Carlton J. Zdanski, Julia S. Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer:
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation. - Depen Morwani, Benjamin L. Edelman, Costin-Andrei Oncescu, Rosie Zhao, Sham M. Kakade:
Feature emergence via margin maximization: case studies in algebraic tasks. - Quentin Bertrand, Avishek Joey Bose, Alexandre Duplessis, Marco Jiralerspong, Gauthier Gidel:
On the Stability of Iterative Retraining of Generative Models on their own Data. - Priyank Jaini, Kevin Clark, Robert Geirhos:
Intriguing Properties of Generative Classifiers. - Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier:
Fast Imitation via Behavior Foundation Models. - Yucheng Yang, Tianyi Zhou, Qiang He, Lei Han, Mykola Pechenizkiy, Meng Fang:
Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning. - Kun Wang, Hao Wu, Yifan Duan, Guibin Zhang, Kai Wang, Xiaojiang Peng, Yu Zheng, Yuxuan Liang, Yang Wang:
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling. - Ling Pan, Moksh Jain, Kanika Madan, Yoshua Bengio:
Pre-Training and Fine-Tuning Generative Flow Networks. - Weigao Sun, Zhen Qin, Weixuan Sun, Shidi Li, Dong Li, Xuyang Shen, Yu Qiao, Yiran Zhong:
CO2: Efficient Distributed Training with Full Communication-Computation Overlap. - Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, Romann M. Weber:
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling. - Wenbo Li, Xin Yu, Kun Zhou, Yibing Song, Zhe Lin:
Image Inpainting via Iteratively Decoupled Probabilistic Modeling. - Yongchao Du, Min Wang, Wengang Zhou, Shuping Hui, Houqiang Li:
Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval. - Neta Shaul, Juan C. Pérez, Ricky T. Q. Chen, Ali K. Thabet, Albert Pumarola, Yaron Lipman:
Bespoke Solvers for Generative Flow Models. - Antoine Bambade, Fabian Schramm, Adrien B. Taylor, Justin Carpentier:
Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning. - Stéphane d'Ascoli, Sören Becker, Philippe Schwaller, Alexander Mathis, Niki Kilbertus:
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. - Shi Fu, Fengxiang He, Xinmei Tian, Dacheng Tao:
Convergence of Bayesian Bilevel Optimization. - Kaizhi Yang, Xiaoshuai Zhang, Zhiao Huang, Xuejin Chen, Zexiang Xu, Hao Su:
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field. - Ilyes Batatia, Lars L. Schaaf, Gábor Csányi, Christoph Ortner, Felix A. Faber:
Equivariant Matrix Function Neural Networks. - Jiatong Shi, Hirofumi Inaguma, Xutai Ma, Ilia Kulikov, Anna Y. Sun:
Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction. - Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Input-gradient space particle inference for neural network ensembles. - Yilan Zhang, Yingxue Xu, Jianqi Chen, Fengying Xie, Hao Chen:
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction. - Yinya Huang, Xiaohan Lin, Zhengying Liu, Qingxing Cao, Huajian Xin, Haiming Wang, Zhenguo Li, Linqi Song, Xiaodan Liang:
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data. - Chenhao Li, Elijah Stanger-Jones, Steve Heim, Sangbae Kim:
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning. - Xiong Xu, Kunzhe Huang, Yiming Li, Zhan Qin, Kui Ren:
Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features. - Young-Jae Park, Minseok Seo, Doyi Kim, Hyeri Kim, Sanghoon Choi, Beomkyu Choi, Jeongwon Ryu, Sohee Son, Hae-Gon Jeon, Yeji Choi:
Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data. - Jiawei Liang, Siyuan Liang, Aishan Liu, Xiaojun Jia, Junhao Kuang, Xiaochun Cao:
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection. - Zeqi Xiao, Tai Wang, Jingbo Wang, Jinkun Cao, Wenwei Zhang, Bo Dai, Dahua Lin, Jiangmiao Pang:
Unified Human-Scene Interaction via Prompted Chain-of-Contacts. - Hangting Ye, Wei Fan, Xiaozhuang Song, Shun Zheng, He Zhao, Dandan Guo, Yi Chang:
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration. - YongKyung Oh, Dongyoung Lim, Sungil Kim:
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data. - Jingcheng Niu, Andrew Liu, Zining Zhu, Gerald Penn:
What does the Knowledge Neuron Thesis Have to do with Knowledge? - Jadie Adams, Shireen Y. Elhabian:
Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds. - Huaxiu Yao, Xinyu Yang, Xinyi Pan, Shengchao Liu, Pang Wei Koh, Chelsea Finn:
Improving Domain Generalization with Domain Relations. - Wufei Ma, Qihao Liu, Jiahao Wang, Angtian Wang, Xiaoding Yuan, Yi Zhang, Zihao Xiao, Guofeng Zhang, Beijia Lu, Ruxiao Duan, Yongrui Qi, Adam Kortylewski, Yaoyao Liu, Alan L. Yuille:
Generating Images with 3D Annotations Using Diffusion Models. - Gérard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath:
High-dimensional SGD aligns with emerging outlier eigenspaces. - Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun, Hongxia Yang:
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis. - Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, Yu Su:
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts. - Minyoung Kim, Timothy M. Hospedales:
A Hierarchical Bayesian Model for Few-Shot Meta Learning. - Andrew Engel, Zhichao Wang, Natalie Frank, Ioana Dumitriu, Sutanay Choudhury, Anand D. Sarwate, Tony Chiang:
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models. - Anastasios Nikolas Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, Tal Schuster:
Conformal Risk Control. - Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia:
RetroBridge: Modeling Retrosynthesis with Markov Bridges. - Chenguo Lin, Yadong Mu:
InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior. - Sigal Raab, Inbal Leibovitch, Guy Tevet, Moab Arar, Amit Haim Bermano, Daniel Cohen-Or:
Single Motion Diffusion. - Hongpeng Cao, Yanbing Mao, Lui Sha, Marco Caccamo:
Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings. - Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian:
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation. - Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan:
SaProt: Protein Language Modeling with Structure-aware Vocabulary. - Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Zhongdao Wang, James T. Kwok, Ping Luo, Huchuan Lu, Zhenguo Li:
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. - Yang Bai, Xinxing Xu, Yong Liu, Salman Khan, Fahad Shahbaz Khan, Wangmeng Zuo, Rick Siow Mong Goh, Chun-Mei Feng:
Sentence-level Prompts Benefit Composed Image Retrieval. - Tailin Wu, Takashi Maruyama, Long Wei, Tao Zhang, Yilun Du, Gianluca Iaccarino, Jure Leskovec:
Compositional Generative Inverse Design. - Sejun Park, Sanghyuk Chun, Wonyeol Lee:
What does automatic differentiation compute for neural networks? - Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, Ping Luo:
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models. - Haoxuan You, Haotian Zhang, Zhe Gan, Xianzhi Du, Bowen Zhang, Zirui Wang, Liangliang Cao, Shih-Fu Chang, Yinfei Yang:
Ferret: Refer and Ground Anything Anywhere at Any Granularity. - Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu:
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. - Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh R. N., Zeyuan Chen, Jianguo Zhang, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese:
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization. - Stylianos Poulakakis-Daktylidis, Hadi Jamali Rad:
BECLR: Batch Enhanced Contrastive Few-Shot Learning. - Josh Alman, Zhao Song:
How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation. - Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, Xiangwen Kong, Xiangyu Zhang, Kaisheng Ma, Li Yi:
DreamLLM: Synergistic Multimodal Comprehension and Creation. - Po-Chen Ko, Jiayuan Mao, Yilun Du, Shao-Hua Sun, Joshua B. Tenenbaum:
Learning to Act from Actionless Videos through Dense Correspondences. - Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak:
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. - Elias Frantar, Carlos Riquelme Ruiz, Neil Houlsby, Dan Alistarh, Utku Evci:
Scaling Laws for Sparsely-Connected Foundation Models. - Joe Benton, Valentin De Bortoli, Arnaud Doucet, George Deligiannidis:
Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization. - Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang:
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models. - Junsheng Zhou, Jinsheng Wang, Baorui Ma, Yu-Shen Liu, Tiejun Huang, Xinlong Wang:
Uni3D: Exploring Unified 3D Representation at Scale. - Siyuan Qi, Shuo Chen, Yexin Li, Xiangyu Kong, Junqi Wang, Bangcheng Yang, Pring Wong, Yifan Zhong, Xiaoyuan Zhang, Zhaowei Zhang, Nian Liu, Yaodong Yang, Song-Chun Zhu:
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents. - Simon Ging, María Alejandra Bravo, Thomas Brox:
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy. - Xuelun Shen, Zhipeng Cai, Wei Yin, Matthias Müller, Zijun Li, Kaixuan Wang, Xiaozhi Chen, Cheng Wang:
GIM: Learning Generalizable Image Matcher From Internet Videos. - Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, Wenping Wang:
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image. - Yufeng Zhang, Hang Yu, Jianguo Li, Weiyao Lin:
Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression. - Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, Maosong Sun:
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs. - Yuanqing Huang, Yinggui Wang, Le Yang, Lei Wang:
Enhanced Face Recognition using Intra-class Incoherence Constraint. - Jonghyun Lee, Dahuin Jung, Saehyung Lee, Junsung Park, Juhyeon Shin, Uiwon Hwang, Sungroh Yoon:
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors. - Wenlong Zhang, Xiaohui Li, Xiangyu Chen, Xiaoyun Zhang, Yu Qiao, Xiao-Ming Wu, Chao Dong:
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution. - Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao:
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood. - Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen:
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning. - Shahriar Golchin, Mihai Surdeanu:
Time Travel in LLMs: Tracing Data Contamination in Large Language Models. - Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal:
Variational Inference for SDEs Driven by Fractional Noise. - Pierre Marion, Yu-Han Wu, Michael Eli Sander, Gérard Biau:
Implicit regularization of deep residual networks towards neural ODEs. - Meng-Chieh Lee, Haiyang Yu, Jian Zhang, Vassilis N. Ioannidis, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos:
NetInfoF Framework: Measuring and Exploiting Network Usable Information. - Yaoming Wang, Jin Li, Xiaopeng Zhang, Bowen Shi, Chenglin Li, Wenrui Dai, Hongkai Xiong, Qi Tian:
BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation. - Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park:
Local Search GFlowNets. - Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan:
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. - Tongda Xu, Ziran Zhu, Dailan He, Yanghao Li, Lina Guo, Yuanyuan Wang, Zhe Wang, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang:
Idempotence and Perceptual Image Compression. - Chengrui Li, Yule Wang, Weihan Li, Anqi Wu:
Forward χ2 Divergence Based Variational Importance Sampling. - Nirmit Joshi, Gal Vardi, Nathan Srebro:
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. - Zhiqiu Xu, Yanjie Chen, Kirill Vishniakov, Yida Yin, Zhiqiang Shen, Trevor Darrell, Lingjie Liu, Zhuang Liu:
Initializing Models with Larger Ones. - Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, Kai Zhang:
DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model. - Haoheng Lan, Jindong Gu, Philip Torr, Hengshuang Zhao:
Influencer Backdoor Attack on Semantic Segmentation. - Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, Kai Zhang:
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction. - Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang:
Procedural Fairness Through Decoupling Objectionable Data Generating Components. - Xinghang Li, Minghuan Liu, Hanbo Zhang, Cunjun Yu, Jie Xu, Hongtao Wu, Chilam Cheang, Ya Jing, Weinan Zhang, Huaping Liu, Hang Li, Tao Kong:
Vision-Language Foundation Models as Effective Robot Imitators. - Niklas Muennighoff, Qian Liu, Armel Randy Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre:
OctoPack: Instruction Tuning Code Large Language Models. - Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Chunyi Li, Wenxiu Sun, Qiong Yan, Guangtao Zhai, Weisi Lin:
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision. - Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long:
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting. - Weian Mao, Muzhi Zhu, Zheng Sun, Shuaike Shen, Lin Yuanbo Wu, Hao Chen, Chunhua Shen:
De novo Protein Design Using Geometric Vector Field Networks. - Jingyang Qiao, Zhizhong Zhang, Xin Tan, Chengwei Chen, Yanyun Qu, Yong Peng, Yuan Xie:
Prompt Gradient Projection for Continual Learning. - Mengyuan Chen, Junyu Gao, Changsheng Xu:
R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning. - Yibing Liu, Chris Xing Tian, Haoliang Li, Lei Ma, Shiqi Wang:
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization. - Marko Mihajlovic, Sergey Prokudin, Marc Pollefeys, Siyu Tang:
ResFields: Residual Neural Fields for Spatiotemporal Signals. - Nicklas Hansen, Hao Su, Xiaolong Wang:
TD-MPC2: Scalable, Robust World Models for Continuous Control. - Xinmeng Huang, Ping Li, Xiaoyun Li:
Stochastic Controlled Averaging for Federated Learning with Communication Compression. - Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, Bo Dai:
AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning. - Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, Zhe Gan:
Guiding Instruction-based Image Editing via Multimodal Large Language Models. - Bingchen Zhao, Haoqin Tu, Chen Wei, Jieru Mei, Cihang Xie:
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning. - Zhengyi Luo, Jinkun Cao, Josh Merel, Alexander Winkler, Jing Huang, Kris M. Kitani, Weipeng Xu:
Universal Humanoid Motion Representations for Physics-Based Control. - Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, Kristian Kersting:
Adaptive Rational Activations to Boost Deep Reinforcement Learning. - Diyang Li, Charles Ling, Zhiqiang Xu, Huan Xiong, Bin Gu:
Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s). - Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell:
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game. - Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan:
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. - François Charton:
Learning the greatest common divisor: explaining transformer predictions. - Steeven Janny, Madiha Nadri, Julie Digne, Christian Wolf:
Space and time continuous physics simulation from partial observations. - Shaofei Cai, Bowei Zhang, Zihao Wang, Xiaojian Ma, Anji Liu, Yitao Liang:
GROOT: Learning to Follow Instructions by Watching Gameplay Videos. - Ganlin Yang, Guoqiang Wei, Zhizheng Zhang, Yan Lu, Dong Liu:
Mask-Based Modeling for Neural Radiance Fields. - Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, Minlie Huang:
Large Language Models Are Not Robust Multiple Choice Selectors. - Aryaman Reddi, Maximilian Tölle, Jan Peters, Georgia Chalvatzaki, Carlo D'Eramo:
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula. - Juncheng Li, Kaihang Pan, Zhiqi Ge, Minghe Gao, Wei Ji, Wenqiao Zhang, Tat-Seng Chua, Siliang Tang, Hanwang Zhang, Yueting Zhuang:
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions. - Sen Cui, Abudukelimu Wuerkaixi, Weishen Pan, Jian Liang, Lei Fang, Changshui Zhang, Fei Wang:
CLAP: Collaborative Adaptation for Patchwork Learning. - Xinyu Shi, Jianhao Ding, Zecheng Hao, Zhaofei Yu:
Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework. - Yaoyu Zhu, Jianhao Ding, Tiejun Huang, Xiaodong Xie, Zhaofei Yu:
Online Stabilization of Spiking Neural Networks. - Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar, Jean-Philippe Thiran, Tinne Tuytelaars:
CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping. - Zhenhui Ye, Tianyun Zhong, Yi Ren, Jiaqi Yang, Weichuang Li, Jiawei Huang, Ziyue Jiang, Jinzheng He, Rongjie Huang, Jinglin Liu, Chen Zhang, Xiang Yin, Zejun Ma, Zhou Zhao:
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis.
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