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ORCID41st ICML 2024: Vienna, Austria
- Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net 2024
Accept (Oral)
- Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Baker Grosse:
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo. - Edward Hughes, Michael D. Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel:
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. - Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal:
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. - Da Xiao, Qingye Meng, Shengping Li, Xingyuan Yuan:
Improving Transformers with Dynamically Composable Multi-Head Attention. - Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber:
Learning Useful Representations of Recurrent Neural Network Weight Matrices. - Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, Qingfu Zhang:
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model. - Ziniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf, Yisong Yue, David A. Ross, Cordelia Schmid, Alireza Fathi:
SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code. - Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao:
Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning. - Jan E. Gerken, Pan Kessel:
Emergent Equivariance in Deep Ensembles. - Linyuan Gong, Sida Wang, Mostafa Elhoushi, Alvin Cheung:
Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks. - Younghyo Park, Gabriel B. Margolis, Pulkit Agrawal:
Position: Automatic Environment Shaping is the Next Frontier in RL. - Qiankun Zhang, Aocheng Shen, Boyu Zhang, Hanrui Jiang, Bingqian Du:
Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning. - Jessica Dai:
Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis. - Juno Kim, Taiji Suzuki:
Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape. - Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou:
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. - Siqi Miao, Zhiyuan Lu, Mia Liu, Javier M. Duarte, Pan Li:
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics. - Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo:
Unified Training of Universal Time Series Forecasting Transformers. - Lucas Spangher, Allen M. Wang, Andrew Maris, Myles Stapelberg, Viraj Mehta, Alex Saperstein, Stephen Lane-Walsh, Akshata Kishore Moharir, Alessandro Pau, Cristina Rea:
Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy. - Christian Schlarmann, Naman Deep Singh, Francesco Croce, Matthias Hein:
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models. - Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, Chuan Shi:
Less is More: on the Over-Globalizing Problem in Graph Transformers. - Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian:
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection. - Charlie Hou, Akshat Shrivastava, Hongyuan Zhan, Rylan Conway, Trang Le, Adithya Sagar, Giulia Fanti, Daniel Lazar:
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. - Can Yaras, Peng Wang, Laura Balzano, Qing Qu
:
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. - Anka Reuel, Lisa Soder, Benjamin Bucknall, Trond Arne Undheim:
Position: Technical Research and Talent is Needed for Effective AI Governance. - Xin Du, Lixin Xiu, Kumiko Tanaka-Ishii:
Bottleneck-Minimal Indexing for Generative Document Retrieval. - Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily B. Fox:
Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response. - Jiayi Chen, Aidong Zhang:
FedMBridge: Bridgeable Multimodal Federated Learning. - Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths:
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? - Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeffrey Wu:
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision. - Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu:
ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization. - Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann:
Expressivity and Generalization: Fragment-Biases for Molecular GNNs. - Sepanta Zeighami, Cyrus Shahabi:
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability. - Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson:
Position: A Safe Harbor for AI Evaluation and Red Teaming. - Kiarash Banihashem, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh:
A Dynamic Algorithm for Weighted Submodular Cover Problem. - Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia:
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits. - Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye, Weilin Liu, Zhiyu Mei, Guangju Wang, Chao Yu, Yi Wu:
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study. - Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal:
Trained Random Forests Completely Reveal your Dataset. - Uijeong Jang, Jason D. Lee, Ernest K. Ryu:
LoRA Training in the NTK Regime has No Spurious Local Minima. - Jayesh Singla, Ananye Agarwal, Deepak Pathak:
SAPG: Split and Aggregate Policy Gradients. - Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private are DP-SGD Implementations? - Qianlong Wen, Mingxuan Ju, Zhongyu Ouyang, Chuxu Zhang, Yanfang Ye:
From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble. - Aaron Lou, Chenlin Meng, Stefano Ermon:
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution. - Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz:
Discovering Environments with XRM. - Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos:
Fast Co-Training under Weak Dependence via Stream-Based Active Learning. - Masahiro Kato, Akihiro Oga, Wataru Komatsubara, Ryo Inokuchi:
Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choice. - Francesco Paissan, Mirco Ravanelli, Cem Subakan:
Listenable Maps for Audio Classifiers. - Barna Saha, Christopher Ye:
I/O Complexity of Attention, or How Optimal is FlashAttention? - Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, Yang Zhang:
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling. - Allen Tran, Aurélien Bibaut, Nathan Kallus:
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments. - Ta Duy Nguyen, Alina Ene:
Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems. - Manuel Glöckler, Michael Deistler, Christian Dietrich Weilbach, Frank Wood, Jakob H. Macke:
All-in-one simulation-based inference. - Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis:
Environment Design for Inverse Reinforcement Learning. - Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras:
Scalable AI Safety via Doubly-Efficient Debate. - Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang, Huazhe Xu, Xianyuan Zhan:
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts. - Chendi Wang, Yuqing Zhu, Weijie J. Su, Yu-Xiang Wang:
Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning. - Ziyad Oulhaj, Mathieu Carrière, Bertrand Michel:
Differentiable Mapper for Topological Optimization of Data Representation. - Wenshuo Li, Xinghao Chen, Han Shu, Yehui Tang, Yunhe Wang:
ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking. - Haoran Li, Zicheng Zhang, Wang Luo, Congying Han, Yudong Hu, Tiande Guo, Shichen Liao:
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error. - Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovic:
Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models. - Dongping Chen, Ruoxi Chen, Shilin Zhang, Yaochen Wang, Yinuo Liu, Huichi Zhou, Qihui Zhang, Yao Wan, Pan Zhou, Lichao Sun:
MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark. - Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, Xing Xie:
CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents. - Simon Buchholz, Bernhard Schölkopf:
Robustness of Nonlinear Representation Learning. - Mustapha Bounoua, Giulio Franzese, Pietro Michiardi:
SΩI: Score-based O-INFORMATION Estimation. - Uri Sherman, Alon Cohen, Tomer Koren, Yishay Mansour:
Rate-Optimal Policy Optimization for Linear Markov Decision Processes. - Danni Yang, Jiayi Ji, Yiwei Ma, Tianyu Guo, Haowei Wang, Xiaoshuai Sun, Rongrong Ji:
SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation. - Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schröder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Thomas Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob N. Foerster:
Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI. - Di Wu, Wasi Uddin Ahmad, Dejiao Zhang, Murali Krishna Ramanathan, Xiaofei Ma:
Repoformer: Selective Retrieval for Repository-Level Code Completion. - Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Robin Rombach:
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis. - Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Peter Cihon, Aspen K. Hopkins, Kevin Bankston, Stella Biderman, Miranda Bogen, Rumman Chowdhury, Alex Engler, Peter Henderson, Yacine Jernite, Seth Lazar, Stefano Maffulli, Alondra Nelson, Joelle Pineau, Aviya Skowron, Dawn Song, Victor Storchan, Daniel Zhang, Daniel E. Ho, Percy Liang, Arvind Narayanan:
Position: On the Societal Impact of Open Foundation Models. - Zachary Novack, Julian J. McAuley, Taylor Berg-Kirkpatrick, Nicholas J. Bryan:
DITTO: Diffusion Inference-Time T-Optimization for Music Generation. - Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park:
Parameterized Physics-informed Neural Networks for Parameterized PDEs. - Yifan Xia, Xianliang Yang, Zichuan Liu, Zhihao Liu, Lei Song, Jiang Bian:
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems. - Gauthier Guinet, Behrooz Omidvar-Tehrani, Anoop Deoras, Laurent Callot:
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation. - Ruijie Zheng, Ching-An Cheng, Hal Daumé III, Furong Huang, Andrey Kolobov:
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control. - Jia Shi, Gautam Rajendrakumar Gare, Jinjin Tian, Siqi Chai, Zhiqiu Lin, Arun Balajee Vasudevan, Di Feng, Francesco Ferroni, Shu Kong:
LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies. - Tijana Zrnic, Emmanuel J. Candès:
Active Statistical Inference. - Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli Shama Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue:
Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion. - Zeqian Ju, Yuancheng Wang, Kai Shen, Xu Tan, Detai Xin, Dongchao Yang, Eric Liu, Yichong Leng, Kaitao Song, Siliang Tang, Zhizheng Wu, Tao Qin, Xiangyang Li, Wei Ye, Shikun Zhang, Jiang Bian, Lei He, Jinyu Li, Sheng Zhao:
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models. - Jiachun Li, Kaining Shi, David Simchi-Levi:
Privacy Preserving Adaptive Experiment Design. - Lingfeng Shen, Aayush Mishra, Daniel Khashabi:
Position: Do pretrained Transformers Learn In-Context by Gradient Descent? - Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, Anca D. Dragan:
Learning to Model the World With Language. - Riley Simmons-Edler, Ryan Paul Badman, Shayne Longpre, Kanaka Rajan:
Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research. - Vassilis Papadopoulos, Jérémie Wenger, Clément Hongler:
Arrows of Time for Large Language Models. - Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, Brian Ichter:
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator. - Pratik Rathore, Weimu Lei, Zachary Frangella, Lu Lu, Madeleine Udell:
Challenges in Training PINNs: A Loss Landscape Perspective. - Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan, Fabien Roger:
AI Control: Improving Safety Despite Intentional Subversion. - Bowen Zhao, Hannaneh Hajishirzi, Qingqing Cao:
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference. - Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller:
Offline Actor-Critic Reinforcement Learning Scales to Large Models. - Zach Evans, CJ Carr, Josiah Taylor, Scott H. Hawley, Jordi Pons:
Fast Timing-Conditioned Latent Audio Diffusion. - Kai Zhang, Yi Luan, Hexiang Hu, Kenton Lee, Siyuan Qiao, Wenhu Chen, Yu Su, Ming-Wei Chang:
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions. - Max Dupré la Tour, Monika Henzinger, David Saulpic:
Making Old Things New: A Unified Algorithm for Differentially Private Clustering. - Wei Zhang, Chaoqun Wan, Yonggang Zhang, Yiu-ming Cheung, Xinmei Tian, Xu Shen, Jieping Ye:
Interpreting and Improving Large Language Models in Arithmetic Calculation. - Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang:
SparseTSF: Modeling Long-term Time Series Forecasting with *1k* Parameters. - Zijian Liu, Zhengyuan Zhou:
On the Last-Iterate Convergence of Shuffling Gradient Methods. - Bowen Jing, Bonnie Berger, Tommi S. Jaakkola:
AlphaFold Meets Flow Matching for Generating Protein Ensembles. - Sajjad Zarifzadeh, Philippe Liu, Reza Shokri:
Low-Cost High-Power Membership Inference Attacks. - Letian Fu, Gaurav Datta, Huang Huang, William Chung-Ho Panitch, Jaimyn Drake, Joseph Ortiz, Mustafa Mukadam, Mike Lambeta, Roberto Calandra, Ken Goldberg:
A Touch, Vision, and Language Dataset for Multimodal Alignment. - Dora Zhao, Jerone T. A. Andrews, Orestis Papakyriakopoulos, Alice Xiang:
Position: Measure Dataset Diversity, Don't Just Claim It. - Zoe Piran, Michal Klein, James Thornton, Marco Cuturi:
Contrasting Multiple Representations with the Multi-Marginal Matching Gap. - Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng:
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data. - Haonan Wang, Qianli Shen, Yao Tong, Yang Zhang, Kenji Kawaguchi:
The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline. - Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr:
Stealing part of a production language model. - Hyunin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi:
Pausing Policy Learning in Non-stationary Reinforcement Learning. - Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, Ethan Perez:
Debating with More Persuasive LLMs Leads to More Truthful Answers. - Sanyam Agarwal, Markus Bläser:
Probabilistic Generating Circuits - Demystified. - Mikel Malagon, Josu Ceberio, José Antonio Lozano:
Self-Composing Policies for Scalable Continual Reinforcement Learning. - Jian Xu, Delu Zeng, John W. Paisley:
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference. - Feihu Huang:
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization. - Li Sun, Zhenhao Huang, Hao Peng, Yujie Wang, Chunyang Liu, Philip S. Yu:
LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering. - Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, Lu Jiang:
VideoPoet: A Large Language Model for Zero-Shot Video Generation. - Yang Jin, Zhicheng Sun, Kun Xu, Kun Xu, Liwei Chen, Hao Jiang, Quzhe Huang, Chengru Song, Yuliang Liu, Di Zhang, Yang Song, Kun Gai, Yadong Mu:
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization. - Florian Tramèr, Gautam Kamath, Nicholas Carlini:
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining. - Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Yao Yao, Luc Van Gool:
Stereo Risk: A Continuous Modeling Approach to Stereo Matching. - Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low:
Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization. - Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy:
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing. - Yunfan Li, Peng Hu, Dezhong Peng, Jiancheng Lv, Jianping Fan, Xi Peng:
Image Clustering with External Guidance. - Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K. Kummerfeld, Rada Mihalcea:
A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity. - Yang Zhang, Zhewei Wei, Ye Yuan, Chongxuan Li, Wenbing Huang:
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction. - Heting Gao, Kaizhi Qian, Junrui Ni, Chuang Gan, Mark A. Hasegawa-Johnson, Shiyu Chang, Yang Zhang:
Speech Self-Supervised Learning Using Diffusion Model Synthetic Data. - Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola:
Position: The Platonic Representation Hypothesis. - Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai:
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks. - Sucheng Ren, Zeyu Wang, Hongru Zhu, Junfei Xiao, Alan L. Yuille, Cihang Xie:
Rejuvenating image-GPT as Strong Visual Representation Learners. - Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik:
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. - Zhengyang Hu, Song Kang, Qunsong Zeng, Kaibin Huang, Yanchao Yang:
InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization. - Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, Tat-Seng Chua:
NExT-GPT: Any-to-Any Multimodal LLM. - Yeonhong Park, Jake Hyun, SangLyul Cho, Bonggeun Sim, Jae W. Lee:
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs. - Uri Stemmer:
Private Truly-Everlasting Robust-Prediction. - Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
ViP: A Differentially Private Foundation Model for Computer Vision. - Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel:
Genie: Generative Interactive Environments. - Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux, Giuseppe Paolo, Themis Palpanas, Ievgen Redko:
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention. - Zhengqi Pei, Anran Zhang, Shuhui Wang, Xiangyang Ji, Qingming Huang:
Data-free Neural Representation Compression with Riemannian Neural Dynamics. - Hao Fei, Shengqiong Wu, Wei Ji, Hanwang Zhang, Meishan Zhang, Mong-Li Lee, Wynne Hsu:
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition. - Shuaicheng Niu, Chunyan Miao, Guohao Chen, Pengcheng Wu, Peilin Zhao:
Test-Time Model Adaptation with Only Forward Passes. - Nianzu Yang, Kaipeng Zeng, Haotian Lu, Yexin Wu, Zexin Yuan, Danni Chen, Shengdian Jiang, Jiaxiang Wu, Yimin Wang, Junchi Yan:
MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation. - Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov:
Flextron: Many-in-One Flexible Large Language Model. - Sungwoo Park, Dongjun Kim, Ahmed Alaa:
Mean-field Chaos Diffusion Models. - Wenjie Xu, Wenbin Wang, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones:
Principled Preferential Bayesian Optimization. - Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu:
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models. - Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang, Shouda Liu, Jie Luo, Xianglong Liu, Michele Magno:
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention. - Hanting Chen, Liuzhi Cheng, Xutao Wang, Yuchuan Tian, Yunhe Wang:
DiJiang: Efficient Large Language Models through Compact Kernelization. - Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
GPTSwarm: Language Agents as Optimizable Graphs. - Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Min-Hung Chen:
DoRA: Weight-Decomposed Low-Rank Adaptation. - Florian Karl, Lukas Malte Kemeter, Gabriel Dax, Paulina Sierak:
Position: Embracing Negative Results in Machine Learning. - Xiuwen Gong, Nitin Bisht, Guandong Xu:
Does Label Smoothing Help Deep Partial Label Learning?
Accept (Spotlight)
- Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti, James Zou, Atri Rudra, Christopher Ré:
Simple linear attention language models balance the recall-throughput tradeoff. - Ali Shirali, Rediet Abebe, Moritz Hardt:
Allocation Requires Prediction Only if Inequality Is Low. - Hongbin Pei, Yu Li, Huiqi Deng, Jingxin Hai, Pinghui Wang, Jie Ma, Jing Tao, Yuheng Xiong, Xiaohong Guan:
Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing. - Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai:
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models. - Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár:
Position: Understanding LLMs Requires More Than Statistical Generalization. - Baoying Chen, Jishen Zeng, Jianquan Yang, Rui Yang:
DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images. - Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger:
Explaining Probabilistic Models with Distributional Values. - Haoxuan Li, Chunyuan Zheng, Shuyi Wang, Kunhan Wu, Eric Hao Wang, Peng Wu, Zhi Geng, Xu Chen, Xiao-Hua Zhou:
Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering. - Yuanbiao Gou, Haiyu Zhao, Boyun Li, Xinyan Xiao, Xi Peng:
Test-Time Degradation Adaptation for Open-Set Image Restoration. - Anqi Mao, Mehryar Mohri, Yutao Zhong:
Regression with Multi-Expert Deferral. - Yuxuan Yin, Yu Wang, Peng Li:
High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling. - Som Sagar, Aditya Taparia, Ransalu Senanayake:
Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models. - Weiyu Chen, James T. Kwok:
Efficient Pareto Manifold Learning with Low-Rank Structure. - Jun-Peng Jiang, Han-Jia Ye, Leye Wang, Yang Yang, Yuan Jiang, De-Chuan Zhan:
Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images. - Václav Vorácek, Tomás Werner:
Convergence of Some Convex Message Passing Algorithms to a Fixed Point. - Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine:
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. - Shuai Zhang, Chuan Zhou, Yang Aron Liu, Peng Zhang, Xixun Lin, Zhi-Ming Ma:
Neural Jump-Diffusion Temporal Point Processes. - Louis Sharrock, Jack Simons, Song Liu, Mark Beaumont:
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models. - Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, Douwe Kiela:
Model Alignment as Prospect Theoretic Optimization. - Guillaume Sanchez, Alexander Spangher, Honglu Fan, Elad Levi, Stella Biderman:
Stay on Topic with Classifier-Free Guidance. - Haocheng Xi, Yuxiang Chen, Kang Zhao, Kai Jun Teh, Jianfei Chen, Jun Zhu:
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization. - Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen:
QuRating: Selecting High-Quality Data for Training Language Models. - Ahmed Khaled, Chi Jin:
Tuning-Free Stochastic Optimization. - Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier:
Second-Order Uncertainty Quantification: A Distance-Based Approach. - Jaewook Lee, Hanseul Cho, Chulhee Yun:
Fundamental Benefit of Alternating Updates in Minimax Optimization. - Xiaolong Zou, Xingxing Cao, Xiaojiao Yang, Bo Hong:
Leveraging Attractor Dynamics in Spatial Navigation for Better Language Parsing. - Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin:
Differentially Private Synthetic Data via Foundation Model APIs 2: Text. - Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J. Getzen, Qi Long, Mayur Naik, Ravi B. Parikh, Eric Wong:
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation. - Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang:
Position: Graph Foundation Models Are Already Here. - Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su:
Improved Operator Learning by Orthogonal Attention. - Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana:
Exploiting Code Symmetries for Learning Program Semantics. - Lang Feng, Pengjie Gu, Bo An, Gang Pan:
Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree. - Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden:
Stochastic Interpolants with Data-Dependent Couplings. - Amit Attia, Tomer Koren:
How Free is Parameter-Free Stochastic Optimization? - Clayton Sanford, Daniel Hsu, Matus Telgarsky:
Transformers, parallel computation, and logarithmic depth. - Weike Fang, Zhejian Zhou, Junzhou He, Weihang Wang:
StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation. - Muhammad Qasim Elahi, Lai Wei, Murat Kocaoglu, Mahsa Ghasemi:
Adaptive Online Experimental Design for Causal Discovery. - Sungyoon Kim, Mert Pilanci:
Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time. - Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott:
Testing the Feasibility of Linear Programs with Bandit Feedback. - Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. - Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. - Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren:
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. - Sina Akbari, Negar Kiyavash:
Triple Changes Estimator for Targeted Policies. - Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep P. Chinchali:
Time Weaver: A Conditional Time Series Generation Model. - Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Saldyt, Anil Murthy:
Position: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks. - Michael T. Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Thomas Jackson, Samuel Coward, Jakob Nicolaus Foerster:
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning. - Maude Lizaire, Michael Rizvi-Martel, Marawan Gamal Abdel Hameed, Guillaume Rabusseau:
A Tensor Decomposition Perspective on Second-order RNNs. - Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi:
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models. - Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction. - William J. Swartworth, David P. Woodruff:
Fast Sampling-Based Sketches for Tensors. - Jongha Jon Ryu, Gregory W. Wornell:
Gambling-Based Confidence Sequences for Bounded Random Vectors. - Xing Han Lù, Zdenek Kasner, Siva Reddy:
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue. - Shayne Longpre, Robert Mahari, Naana Obeng-Marnu, William Brannon, Tobin South, Katy Ilonka Gero, Alex Pentland, Jad Kabbara:
Position: Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them? - Michael Sun, Minghao Guo, Weize Yuan, Veronika Thost, Crystal Elaine Owens, Aristotle Franklin Grosz, Sharvaa Selvan, Katelyn Zhou, Hassan Mohiuddin, Benjamin J. Pedretti, Zachary P. Smith, Jie Chen, Wojciech Matusik:
Representing Molecules as Random Walks Over Interpretable Grammars. - Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas, Felix Zhou:
Replicable Learning of Large-Margin Halfspaces. - Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi:
Transport of Algebraic Structure to Latent Embeddings. - Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown:
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. - Ning Liu, Yiming Fan, Xianyi Zeng, Milan Klöwer, Lu Zhang, Yue Yu:
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws. - Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus:
Stochastic Localization via Iterative Posterior Sampling. - Nina Vesseron, Marco Cuturi:
On a Neural Implementation of Brenier's Polar Factorization. - Zhiyao Luo, Yangchen Pan, Peter J. Watkinson, Tingting Zhu:
Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination. - Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel:
Decoding-time Realignment of Language Models. - Anastasios Tsiamis, Aren Karapetyan, Yueshan Li, Efe C. Balta, John Lygeros:
Predictive Linear Online Tracking for Unknown Targets. - Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. - Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation. - Tianci Liu, Haoyu Wang, Shiyang Wang, Yu Cheng, Jing Gao:
LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models. - Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch:
Robust and Conjugate Gaussian Process Regression. - Logan Engstrom, Axel Feldmann, Aleksander Madry:
DsDm: Model-Aware Dataset Selection with Datamodels. - Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, Xia Hu:
LLM Maybe LongLM: SelfExtend LLM Context Window Without Tuning. - Liangzu Peng, Wotao Yin:
Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares. - Shahaf Bassan, Guy Amir, Guy Katz:
Local vs. Global Interpretability: A Computational Complexity Perspective. - Congyu Qiao, Ning Xu, Yihao Hu, Xin Geng:
ULAREF: A Unified Label Refinement Framework for Learning with Inaccurate Supervision. - Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli, Matteo Papini:
Learning Optimal Deterministic Policies with Stochastic Policy Gradients. - Davide Legacci, Panayotis Mertikopoulos, Bary S. R. Pradelski:
A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights. - Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. - Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe:
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation. - Shikun Liu, Deyu Zou, Han Zhao, Pan Li:
Pairwise Alignment Improves Graph Domain Adaptation. - Liam Hodgson, Danilo Bzdok:
Estimating Unknown Population Sizes Using the Hypergeometric Distribution. - Andrés Altieri, Marco Romanelli, Georg Pichler, Florence Alberge, Pablo Piantanida:
Beyond the Norms: Detecting Prediction Errors in Regression Models. - Ivana Balazevic, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, Olivier J. Hénaff:
Memory Consolidation Enables Long-Context Video Understanding. - Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li, Stan Z. Li:
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge. - Catalin Mitelut, Benjamin J. Smith, Peter Vamplew:
Position: Intent-aligned AI Systems Must Optimize for Agency Preservation. - Maciej Wolczyk, Bartlomiej Cupial, Mateusz Ostaszewski, Michal Bortkiewicz, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem. - Yangfan Liu, Jiaqi Lv, Xin Geng, Ning Xu:
Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency. - Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He:
Truly No-Regret Learning in Constrained MDPs. - Umberto M. Tomasini, Matthieu Wyart:
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. - Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner:
Position: Mission Critical - Satellite Data is a Distinct Modality in Machine Learning. - Aaron Archer, Matthew Fahrbach, Kuikui Liu, Prakash Prabhu:
Practical Performance Guarantees for Pipelined DNN Inference. - Lucas Theis:
Position: What makes an image realistic? - Ruidong Wu, Ruihan Guo, Rui Wang, Shitong Luo, Yue Xu, Jiahan Li, Jianzhu Ma, Qiang Liu, Yunan Luo, Jian Peng:
FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames. - Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor W. Tsang:
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient. - Zhihai Wang, Lei Chen, Jie Wang, Yinqi Bai, Xing Li, Xijun Li, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu:
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design. - Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. - Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag, Thomas Hofmann:
Navigating Scaling Laws: Compute Optimality in Adaptive Model Training. - Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang:
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. - Chen Xu, Hanyang Jiang, Yao Xie:
Conformal prediction for multi-dimensional time series by ellipsoidal sets. - Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro:
Asymptotics of feature learning in two-layer networks after one gradient-step. - Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung, Hyun Joo Ro, Meeyoung Cha, Ho Min Kim:
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space. - Yulong Huang, Xiaopeng Lin, Hongwei Ren, Haotian Fu, Yue Zhou, Zunchang Liu, Biao Pan, Bojun Cheng:
CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks. - Xu Yang, Huaxiu Yao, Ying Wei:
One Meta-tuned Transformer is What You Need for Few-shot Learning. - Johan Samir Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro:
Mixtures of Experts Unlock Parameter Scaling for Deep RL. - Yunyan Bai, Yuxing Liu, Luo Luo:
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition. - Sascha Xu, Nils Philipp Walter, Janis Kalofolias, Jilles Vreeken:
Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence. - Wang Chi Cheung, Lixing Lyu:
Leveraging (Biased) Information: Multi-armed Bandits with Offline Data. - Anpeng Wu, Haoxuan Li, Kun Kuang, Keli Zhang, Fei Wu:
Learning Causal Relations from Subsampled Time Series with Two Time-Slices. - Yuchao Lin, Jacob Helwig, Shurui Gui, Shuiwang Ji:
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency. - Sayan Bhattacharya, Gramoz Goranci, Shaofeng H.-C. Jiang, Yi Qian, Yubo Zhang:
Dynamic Facility Location in High Dimensional Euclidean Spaces. - Xisen Jin, Xiang Ren:
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement. - Sangmin Lee, Abbas Mammadov, Jong Chul Ye:
Defining Neural Network Architecture through Polytope Structures of Datasets. - Siwei Wei, Xudong Zhang, Zhiyang Zhou, Yan Cai:
Extending Test-Time Augmentation with Metamorphic Relations for Combinatorial Problems. - Xiyu Wang, Baijiong Lin, Daochang Liu, Ying-Cong Chen, Chang Xu:
Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning. - Yu-Hu Yan, Jing Wang, Peng Zhao:
Handling Heterogeneous Curvatures in Bandit LQR Control. - Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani:
Optimal Ridge Regularization for Out-of-Distribution Prediction. - Caixing Wang, Xingdong Feng:
Optimal Kernel Quantile Learning with Random Features. - Zelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang, Gang Wang, Xinyu Xing:
RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation. - Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis:
Prospective Side Information for Latent MDPs. - Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis:
Dynamic Correlation Clustering in Sublinear Update Time. - Boqi Li, Weiwei Liu:
A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks. - Shiming Ge, Weijia Guo, Chenyu Li, Junzheng Zhang, Yong Li, Dan Zeng:
Masked Face Recognition with Generative-to-Discriminative Representations. - Meysam Alishahi, Jeff M. Phillips:
No Dimensional Sampling Coresets for Classification. - Huy Tran, Yikun Bai, Abihith Kothapalli, Ashkan Shahbazi, Xinran Liu, Rocio Diaz Martin, Soheil Kolouri:
Stereographic Spherical Sliced Wasserstein Distances. - Feihu Huang, Jianyu Zhao:
Faster Adaptive Decentralized Learning Algorithms. - Kaihong Zhang, Heqi Yin, Feng Liang, Jingbo Liu:
Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions. - Isha Garg, Deepak Ravikumar, Kaushik Roy:
Memorization Through the Lens of Curvature of Loss Function Around Samples. - Saul José Rodrigues dos Santos, Vlad Niculae, Daniel C. McNamee, André F. T. Martins:
Sparse and Structured Hopfield Networks. - Nikhil Vyas, Depen Morwani, Rosie Zhao, Gal Kaplun, Sham M. Kakade, Boaz Barak:
Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning. - Chao Wang, Xin Bing, Xin He, Caixing Wang:
Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features. - Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, Zhi Geng:
Automating the Selection of Proxy Variables of Unmeasured Confounders. - David Venuto, Mohammad Sami Nur Islam, Martin Klissarov, Doina Precup, Sherry Yang, Ankit Anand:
Code as Reward: Empowering Reinforcement Learning with VLMs. - Shengjie Wang, Shaohuai Liu, Weirui Ye, Jiacheng You, Yang Gao:
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data. - Caixing Wang, Ziliang Shen:
Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery. - Changlong Wu, Yifan Wang, Ananth Grama:
A Theory of Fault-Tolerant Learning. - Andreas Madsen, Siva Reddy, Sarath Chandar:
Faithfulness Measurable Masked Language Models. - Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li:
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations. - Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni:
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. - Jacob Yoke Hong Si, Wendy Yusi Cheng, Michael Cooper, Rahul G. Krishnan:
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation. - Francesco Bertolotti, Walter Cazzola:
By Tying Embeddings You Are Assuming the Distributional Hypothesis. - Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun:
BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition. - Ziao Guo, Yang Li, Chang Liu, Wenli Ouyang, Junchi Yan:
ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation. - Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry:
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. - Taeho Yoon, Jaeyeon Kim, Jaewook J. Suh, Ernest K. Ryu:
Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique. - Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. - Feiran Li, Qianqian Xu, Shilong Bao, Zhiyong Yang, Runmin Cong, Xiaochun Cao, Qingming Huang:
Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection. - Mudit Gaur, Amrit S. Bedi, Di Wang, Vaneet Aggarwal:
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization. - Yongqiang Cai:
Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions. - Jianyu Xu, Yu-Xiang Wang:
Pricing with Contextual Elasticity and Heteroscedastic Valuation. - Lu Bai, Lixin Cui, Ming Li, Yue Wang, Edwin R. Hancock:
QBMK: Quantum-based Matching Kernels for Un-attributed Graphs. - Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, Feng Liu:
Sample-specific Masks for Visual Reprogramming-based Prompting. - Xianghe Pang, Shuo Tang, Rui Ye, Yuxin Xiong, Bolun Zhang, Yanfeng Wang, Siheng Chen:
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation. - Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
Learning Decision Trees and Forests with Algorithmic Recourse. - Ari Karchmer:
On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning. - Yuanbang Liang, Jing Wu, Yu-Kun Lai, Yipeng Qin:
Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling. - Zhen Huang, Jiajin Sun, Yian Huang:
Quasi-Monte Carlo Features for Kernel Approximation. - Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi:
Agnostic Sample Compression Schemes for Regression. - Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim, Nur Muhammad (Mahi) Shafiullah, Lerrel Pinto:
Behavior Generation with Latent Actions. - Kamesh Munagala, Govind S. Sankar:
Individual Fairness in Graph Decomposition. - Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner:
Position: Amazing Things Come From Having Many Good Models. - Guy Ohayon, Tomer Michaeli, Michael Elad:
The Perception-Robustness Tradeoff in Deterministic Image Restoration. - Weixi Song, Zuchao Li, Lefei Zhang, Hai Zhao, Bo Du:
Sparse is Enough in Fine-tuning Pre-trained Large Language Models. - Micah Goldblum, Marc Anton Finzi, Keefer Rowan, Andrew Gordon Wilson:
Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. - Daniel Dodd, Louis Sharrock, Christopher Nemeth:
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds. - Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, Yu Su:
TravelPlanner: A Benchmark for Real-World Planning with Language Agents. - Daniel Barzilai, Ohad Shamir:
Generalization in Kernel Regression Under Realistic Assumptions. - Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang:
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. - Eleni Straitouri, Manuel Gomez Rodriguez:
Designing Decision Support Systems using Counterfactual Prediction Sets. - Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu, Xihui Liu, Wanli Ouyang, Lei Bai:
FiT: Flexible Vision Transformer for Diffusion Model. - Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Hongxin Wei, Tongliang Liu:
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints. - Loh Sher En Jessica, Naheed Anjum Arafat, Wei Xian Lim, Wai Lee Chan, Adams Wai-Kin Kong:
Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation. - Zhiheng Zhang:
Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders. - Shaojie Li, Yong Liu:
Concentration Inequalities for General Functions of Heavy-Tailed Random Variables. - Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos:
Adaptive Proximal Gradient Methods Are Universal Without Approximation. - Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff:
Variational Learning is Effective for Large Deep Networks. - Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang:
Improving Interpretation Faithfulness for Vision Transformers. - Jiakui Hu, Man Yao, Xuerui Qiu, Yuhong Chou, Yuxuan Cai, Ning Qiao, Yonghong Tian, Bo Xu, Guoqi Li:
High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost. - Ankit Pensia:
A Subquadratic Time Algorithm for Robust Sparse Mean Estimation. - Deyi Ji, Feng Zhao, Lanyun Zhu, Wenwei Jin, Hongtao Lu, Jieping Ye:
Discrete Latent Perspective Learning for Segmentation and Detection. - Marco Mussi, Alessandro Montenegro, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli:
Best Arm Identification for Stochastic Rising Bandits. - Sangjun Park, JinYeong Bak:
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture. - Xudong Li, Runze Hu, Jingyuan Zheng, Yan Zhang, Shengchuan Zhang, Xiawu Zheng, Ke Li, Yunhang Shen, Yutao Liu, Pingyang Dai, Rongrong Ji:
Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment. - Yunshan Zhong, Jiawei Hu, You Huang, Yuxin Zhang, Rongrong Ji:
ERQ: Error Reduction for Post-Training Quantization of Vision Transformers. - Shengju Yu, Zhibin Dong, Siwei Wang, Xinhang Wan, Yue Liu, Weixuan Liang, Pei Zhang, Wenxuan Tu, Xinwang Liu:
Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering. - Jie Cheng, Gang Xiong, Xingyuan Dai, Qinghai Miao, Yisheng Lv, Fei-Yue Wang:
RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences. - Xingyu Wan, Chengquan Zhang, Pengyuan Lyu, Sen Fan, Zihan Ni, Kun Yao, Errui Ding, Jingdong Wang:
Towards Unified Multi-granularity Text Detection with Interactive Attention. - Tian-Zuo Wang, Wen-Bo Du, Zhi-Hua Zhou:
An Efficient Maximal Ancestral Graph Listing Algorithm. - Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, Mingsheng Long:
Transolver: A Fast Transformer Solver for PDEs on General Geometries. - Yuzhu Wang, Lechao Cheng, Chaowei Fang, Dingwen Zhang, Manni Duan, Meng Wang:
Revisiting the Power of Prompt for Visual Tuning. - Jian Liang, Lijun Sheng, Zhengbo Wang, Ran He, Tieniu Tan:
Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization. - Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clément Farabet, Shane Legg:
Position: Levels of AGI for Operationalizing Progress on the Path to AGI. - Duc Nguyen, Anderson Ye Zhang:
Novel Spectral Algorithms for the Partial Credit Model. - Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet:
Domain Generalisation via Imprecise Learning. - Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow, Shuicheng Yan, Tat-Seng Chua, Yueting Zhuang, Hanwang Zhang:
Auto-Encoding Morph-Tokens for Multimodal LLM.
Accept (Poster)
- Ruochen Wang, Ting Liu, Cho-Jui Hsieh, Boqing Gong:
On Discrete Prompt Optimization for Diffusion Models. - Hao Dai, Yang Liu, Peng Su, Hecheng Cai, Shudong Huang, Jiancheng Lv:
Multi-View Clustering by Inter-cluster Connectivity Guided Reward. - Michal Nauman, Michal Bortkiewicz, Piotr Milos, Tomasz Trzcinski, Mateusz Ostaszewski, Marek Cygan:
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning. - Matthieu Meeus, Igor Shilov, Manuel Faysse, Yves-Alexandre de Montjoye:
Copyright Traps for Large Language Models. - Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Kacper Mlodozeniec, Tegan Maharaj, David Krueger:
Implicit meta-learning may lead language models to trust more reliable sources. - Hany Hamed, Subin Kim, Dongyeong Kim, Jaesik Yoon, Sungjin Ahn:
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming. - Yang Zhao, Hao Zhang, Xiuyuan Hu:
When Will Gradient Regularization Be Harmful? - Dingzhi Yu, Yunuo Cai, Wei Jiang, Lijun Zhang:
Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond. - Novin Shahroudi, Mihkel Lepson, Meelis Kull:
Evaluation of Trajectory Distribution Predictions with Energy Score. - Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler:
Causal Discovery with Fewer Conditional Independence Tests. - Xuantong Liu, Tianyang Hu, Wenjia Wang, Kenji Kawaguchi, Yuan Yao:
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion. - Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David A. Forsyth, Dan Hendrycks:
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal. - Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu, Haoyu Zhao:
Adversarial Attacks on Combinatorial Multi-Armed Bandits. - Brian K. Chen, Tianyang Hu, Hui Jin, Hwee Kuan Lee, Kenji Kawaguchi:
Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers. - Bonan Zhang, Chia-Yu Chen, Naveen Verma:
Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems. - Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos, Johan A. K. Suykens:
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method. - Yingru Li, Jiawei Xu, Lei Han, Zhi-Quan Luo:
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent. - Khashayar Gatmiry, Zhiyuan Li, Sashank J. Reddi, Stefanie Jegelka:
Simplicity Bias via Global Convergence of Sharpness Minimization. - Narun Krishnamurthi Raman, Taylor Lundy, Samuel Joseph Amouyal, Yoav Levine, Kevin Leyton-Brown, Moshe Tennenholtz:
STEER: Assessing the Economic Rationality of Large Language Models. - Nikhil Sardana, Jacob P. Portes, Sasha Doubov, Jonathan Frankle:
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws. - Ruochen Wang, Sohyun An, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, Cho-Jui Hsieh:
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts. - Kyuwon Kim, Donghwan Kim:
Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements. - Zhongzhi Yu, Zheng Wang, Yonggan Fu, Huihong Shi, Khalid Shaikh, Yingyan Celine Lin:
Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration. - Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljacic, Di Luo:
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision. - Yongxin Li, Mengyuan Liu, You Wu, Xucheng Wang, Xiangyang Yang, Shuiwang Li:
Learning Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking. - Ziwei Jiang, Murat Kocaoglu:
Conditional Common Entropy for Instrumental Variable Testing and Partial Identification. - Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. - Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine:
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. - Elvis Dohmatob, Meyer Scetbon:
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms. - Myungsik Cho, Jongeui Park, Suyoung Lee, Youngchul Sung:
Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling. - Tehila Dahan, Kfir Yehuda Levy:
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training. - Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Jiayuan He, Yinghai Lu, Yu Shi:
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations. - Zongmeng Zhang, Yufeng Shi, Jinhua Zhu, Wengang Zhou, Xiang Qi, Peng Zhang, Houqiang Li:
Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning. - Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin T. Vechev:
Instruction Tuning for Secure Code Generation. - Saeid Naderiparizi, Xiaoxuan Liang, Setareh Cohan, Berend Zwartsenberg, Frank Wood:
Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance. - Suyuan Zhao, Jiahuan Zhang, Yushuai Wu, Yizhen Luo, Zaiqing Nie:
LangCell: Language-Cell Pre-training for Cell Identity Understanding. - Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng:
Causal Representation Learning from Multiple Distributions: A General Setting. - Mingyuan Bai, Wei Huang, Tenghui Li, Andong Wang, Junbin Gao, Cesar F. Caiafa, Qibin Zhao:
Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance. - Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi:
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect. - Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel:
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments. - Li Du, Afra Amini, Lucas Torroba Hennigen, Xinyan Velocity Yu, Holden Lee, Jason Eisner, Ryan Cotterell:
Principled Gradient-Based MCMC for Conditional Sampling of Text. - Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang:
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition. - Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi:
Infinite-Horizon Distributionally Robust Regret-Optimal Control. - Drew Prinster, Samuel Don Stanton, Anqi Liu, Suchi Saria:
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them). - Hyunjong Lee, Yedarm Seong, Sungdong Lee, Joong-Ho Won:
StrWAEs to Invariant Representations. - Xinyu Zhang, Wenjie Qiu, Yi-Chen Li, Lei Yuan, Chengxing Jia, Zongzhang Zhang, Yang Yu:
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics. - Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto:
Linguistic Calibration of Long-Form Generations. - Naman Jain, Manish Shetty, Tianjun Zhang, King Han, Koushik Sen, Ion Stoica:
R2E: Turning any Github Repository into a Programming Agent Environment. - Seunghyun Kim, Seohyeon Jung, Seonghyeon Kim, Juho Lee:
Learning to Explore for Stochastic Gradient MCMC. - Yizhe Huang, Anji Liu, Fanqi Kong, Yaodong Yang, Song-Chun Zhu, Xue Feng:
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning. - Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti:
How to Explore with Belief: State Entropy Maximization in POMDPs. - Piyushi Manupriya, Pratik Jawanpuria, Karthik S. Gurumoorthy, Saketha Nath Jagarlapudi, Bamdev Mishra:
Submodular framework for structured-sparse optimal transport. - Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli:
Graph-Triggered Rising Bandits. - Youngsik Yoon, Gangbok Lee, Sungsoo Ahn, Jungseul Ok:
Breadth-First Exploration on Adaptive Grid for Reinforcement Learning. - Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki:
SILVER: Single-loop variance reduction and application to federated learning. - Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen, Viet Anh Nguyen:
Generative Conditional Distributions by Neural (Entropic) Optimal Transport. - Daniel LeVine, Syed Asad Rizvi, Sacha Lévy, Nazreen Pallikkavaliyaveetil, David Zhang, Xingyu Chen, Sina Ghadermarzi, Ruiming Wu, Zihe Zheng, Ivan Vrkic, Anna Zhong, Daphne Raskin, Insu Han, Antonio Henrique de Oliveira Fonseca, Josue Ortega Caro, Amin Karbasi, Rahul Madhav Dhodapkar, David van Dijk:
Cell2Sentence: Teaching Large Language Models the Language of Biology. - Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Watermark Stealing in Large Language Models. - Tae Hong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, Jaewoong Cho, Juho Lee:
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models. - Billy Joe Franks, Christopher Morris, Ameya Velingker, Floris Geerts:
Weisfeiler-Leman at the margin: When more expressivity matters. - Harshay Shah, Andrew Ilyas, Aleksander Madry:
Decomposing and Editing Predictions by Modeling Model Computation. - Jaemoo Choi, Jaewoong Choi, Myungjoo Kang:
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport. - Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation. - Brian Cho, Kyra Gan, Nathan Kallus:
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams. - Zhaoru Ke, Hang Yu, Jianguo Li, Haipeng Zhang:
DUPLEX: Dual GAT for Complex Embedding of Directed Graphs. - Zixi Wei, Yuzhou Cao, Lei Feng:
Exploiting Human-AI Dependence for Learning to Defer. - Zhiyu Zhu, Huaming Chen, Xinyi Wang, Jiayu Zhang, Zhibo Jin, Jason Xue, Jun Shen:
Iterative Search Attribution for Deep Neural Networks. - Zitao Song, Chao Yang, Chaojie Wang, Bo An, Shuang Li:
Latent Logic Tree Extraction for Event Sequence Explanation from LLMs. - Gaurav Rohit Ghosal, Tatsunori Hashimoto, Aditi Raghunathan:
Understanding Finetuning for Factual Knowledge Extraction. - Peijie Dong, Lujun Li, Zhenheng Tang, Xiang Liu, Xinglin Pan, Qiang Wang, Xiaowen Chu:
Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models. - Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin:
tinyBenchmarks: evaluating LLMs with fewer examples. - Jiajun Ma, Shuchen Xue, Tianyang Hu, Wenjia Wang, Zhaoqiang Liu, Zhenguo Li, Zhi-Ming Ma, Kenji Kawaguchi:
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling. - Zhankun Luo, Abolfazl Hashemi:
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression. - Qi Lv, Hao Li, Xiang Deng, Rui Shao, Michael Y. Wang, Liqiang Nie:
RoboMP2: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models. - Giannis Daras, Alex Dimakis, Constantinos Daskalakis:
Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data. - Yongcheng Zeng, Guoqing Liu, Weiyu Ma, Ning Yang, Haifeng Zhang, Jun Wang:
Token-level Direct Preference Optimization. - Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki:
Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning. - Nikita Durasov, Doruk Öner, Jonathan Donier, Hieu Le, Pascal Fua:
Enabling Uncertainty Estimation in Iterative Neural Networks. - Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré:
Clifford-Steerable Convolutional Neural Networks. - Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré:
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows. - Nayeong Kim, Juwon Kang, Sungsoo Ahn, Jungseul Ok, Suha Kwak:
Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization. - Sergei Shumilin, Alexander Ryabov, Nikolay B. Yavich, Evgeny Burnaev, Vladimir Vanovskiy:
Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation. - Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang:
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction. - Utsav Singh, Wesley A. Suttle, Brian M. Sadler, Vinay P. Namboodiri, Amrit S. Bedi:
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling. - Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski:
Robust Yet Efficient Conformal Prediction Sets. - Qian Tang, Yikai Zhang, Boxiang Wang:
Finite Smoothing Algorithm for High-Dimensional Support Vector Machines and Quantile Regression. - Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno, Marco Guerra, Gabriele Moreno Berton, Carlo Masone:
Scale-Free Image Keypoints Using Differentiable Persistent Homology. - Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, Qingfu Zhang:
Smooth Tchebycheff Scalarization for Multi-Objective Optimization. - Yara Shamshoum, Nitzan Hodos, Yuval Sieradzki, Assaf Schuster:
DNCs Require More Planning Steps. - Hyunki Seong, David Hyunchul Shim:
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity. - Ke Xue, Rong-Xi Tan, Xiaobin Huang, Chao Qian:
Offline Multi-Objective Optimization. - Kishan Panaganti, Adam Wierman, Eric Mazumdar:
Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data. - Yang Liu, Deyu Bo, Chuan Shi:
Graph Distillation with Eigenbasis Matching. - Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frédéric Pascal:
Random matrix theory improved Fréchet mean of symmetric positive definite matrices. - Jian Wang, Xin Lan, Yuxin Tian, Jiancheng Lv:
MS3D: A RG Flow-Based Regularization for GAN Training with Limited Data. - Moritz Stephan, Alexander Khazatsky, Eric Mitchell, Annie S. Chen, Sheryl Hsu, Archit Sharma, Chelsea Finn:
RLVF: Learning from Verbal Feedback without Overgeneralization. - Zhuowen Yuan, Zidi Xiong, Yi Zeng, Ning Yu, Ruoxi Jia, Dawn Song, Bo Li:
RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content. - Praneeth Kacham, Vahab Mirrokni, Peilin Zhong:
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels. - Ren-Jian Wang, Ke Xue, Cong Guan, Chao Qian:
Quality-Diversity with Limited Resources. - Eros Fanì, Raffaello Camoriano, Barbara Caputo, Marco Ciccone:
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers. - Xudong Gong, Dawei Feng, Kele Xu, Yuanzhao Zhai, Chengkang Yao, Weijia Wang, Bo Ding, Huaimin Wang:
Iterative Regularized Policy Optimization with Imperfect Demonstrations. - Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng, Kensen Shi, Charles Sutton, Pengcheng Yin:
NExT: Teaching Large Language Models to Reason about Code Execution. - Nahyuk Lee, Juhong Min, Junha Lee, Seungwook Kim, Kanghee Lee, Jaesik Park, Minsu Cho:
3D Geometric Shape Assembly via Efficient Point Cloud Matching. - He Jia:
Simulation-Based Inference with Quantile Regression. - Hengkai Tan, Songming Liu, Kai Ma, Chengyang Ying, Xingxing Zhang, Hang Su, Jun Zhu:
Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning. - Bingheng Li, Linxin Yang, Yupeng Chen, Senmiao Wang, Haitao Mao, Qian Chen, Yao Ma, Akang Wang, Tian Ding, Jiliang Tang, Ruoyu Sun:
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming. - Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann:
Recurrent Distance Filtering for Graph Representation Learning. - Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki:
Adaptively Perturbed Mirror Descent for Learning in Games. - Yujun Zhou, Yufei Han, Haomin Zhuang, Hongyan Bao, Xiangliang Zhang:
Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data. - Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal:
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization. - Nathan H. Ng, Roger Baker Grosse, Marzyeh Ghassemi:
Measuring Stochastic Data Complexity with Boltzmann Influence Functions. - Chang Chen, Junyeob Baek, Fei Deng, Kenji Kawaguchi, Caglar Gulcehre, Sungjin Ahn:
PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer. - Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, Yoon Kim:
Gated Linear Attention Transformers with Hardware-Efficient Training. - Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin:
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning. - Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
A Language Model's Guide Through Latent Space. - Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar:
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? - Andreas René Geist, Jonas Frey, Mikel Zhobro, Anna Levina, Georg Martius:
Learning with 3D rotations, a hitchhiker's guide to SO(3). - Alfred Nilsson, Klas Wijk, Sai Bharath Chandra Gutha, Erik Englesson, Alexandra Hotti, Carlo Saccardi, Oskar Kviman, Jens Lagergren, Ricardo Vinuesa, Hossein Azizpour:
Indirectly Parameterized Concrete Autoencoders. - Haokun Gui, Xiucheng Li, Xinyang Chen:
Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment. - Hiren Madhu, Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri:
Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms. - Young D. Kwon, Rui Li, Stylianos I. Venieris, Jagmohan Chauhan, Nicholas Donald Lane, Cecilia Mascolo:
TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge. - Kang You, Zekai Xu, Chen Nie, Zhijie Deng, Qinghai Guo, Xiang Wang, Zhezhi He:
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN. - Onur Celik, Aleksandar Taranovic, Gerhard Neumann:
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts. - Huilai Chen, Yuanbo Wen, Limin Cheng, Shouxu Kuang, Yumeng Liu, Weijia Li, Ling Li, Rui Zhang, Xinkai Song, Wei Li, Qi Guo, Yunji Chen:
AutoOS: Make Your OS More Powerful by Exploiting Large Language Models. - Frédéric Zheng, Alexandre Proutière:
Conformal Predictions under Markovian Data. - Luca Franco, Paolo Mandica, Konstantinos Kallidromitis, Devin Guillory, Yu-Teng Li, Trevor Darrell, Fabio Galasso:
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift. - David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom:
Rolling Diffusion Models. - Haoyang Li, Xin Wang, Zeyang Zhang, Haibo Chen, Ziwei Zhang, Wenwu Zhu:
Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization. - Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman, Frank Hutter:
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. - Kui Zhang, Hang Zhou, Jie Zhang, Wenbo Zhou, Weiming Zhang, Nenghai Yu:
Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation. - Haowei Lin, Baizhou Huang, Haotian Ye, Qinyu Chen, Zihao Wang, Sujian Li, Jianzhu Ma, Xiaojun Wan, James Zou, Yitao Liang:
Selecting Large Language Model to Fine-tune via Rectified Scaling Law. - Xingwu Chen, Difan Zou:
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks. - Dipendra Misra, Aldo Pacchiano, Robert E. Schapire:
Provable Interactive Learning with Hindsight Instruction Feedback. - Katie E. Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington:
Scaling Exponents Across Parameterizations and Optimizers. - Mingyu Kim, Jun-Seong Kim, Se-Young Yun, Jin-Hwa Kim:
Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs. - Zhiheng Xi, Wenxiang Chen, Boyang Hong, Senjie Jin, Rui Zheng, Wei He, Yiwen Ding, Shichun Liu, Xin Guo, Junzhe Wang, Honglin Guo, Wei Shen, Xiaoran Fan, Yuhao Zhou, Shihan Dou, Xiao Wang, Xinbo Zhang, Peng Sun, Tao Gui, Qi Zhang, Xuanjing Huang:
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning. - Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jiménez, François Fleuret, Pascal Frossard:
Localizing Task Information for Improved Model Merging and Compression. - Milong Ren, Tian Zhu, Haicang Zhang:
CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model. - Wonje Choi, Woo Kyung Kim, Minjong Yoo, Honguk Woo:
Embodied CoT Distillation From LLM To Off-the-shelf Agents. - Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp, Mathias Niepert:
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations. - Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad:
Nesting Particle Filters for Experimental Design in Dynamical Systems. - Duy Minh Ho Nguyen, Nina Lukashina, Tai Nguyen, An T. Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert:
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. - Denis Blessing, Xiaogang Jia, Johannes Esslinger, Francisco Vargas, Gerhard Neumann:
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling. - My Phan, Kianté Brantley, Stephanie Milani, Soroush Mehri, Gokul Swamy, Geoffrey J. Gordon:
When is Transfer Learning Possible? - Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss:
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. - Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li, Mingduo Zhao, Hiroyasu Iso, Mark J. van der Laan:
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer. - David Stein, Bjoern Andres:
Partial Optimality in the Linear Ordering Problem. - Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael Gastpar:
LASER: Linear Compression in Wireless Distributed Optimization. - Jiewei Zhang, Song Guo, Peiran Dong, Jie Zhang, Ziming Liu, Yue Yu, Xiao-Ming Wu:
Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring. - Jinxia Yang, Bing Su, Xin Zhao, Ji-Rong Wen:
Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training. - Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning:
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data. - Junyoung Seo, Susung Hong, Wooseok Jang, Inès Hyeonsu Kim, Minseop Kwak, Doyup Lee, Seungryong Kim:
Retrieval-Augmented Score Distillation for Text-to-3D Generation. - Haiyan Jiang, Giulia De Masi, Huan Xiong, Bin Gu:
NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks. - Xuefeng Liu, Chih-chan Tien, Peng Ding, Songhao Jiang, Rick L. Stevens:
Entropy-Reinforced Planning with Large Language Models for Drug Discovery. - Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos:
What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis. - Sayak Ray Chowdhury, Anush Kini, Nagarajan Natarajan:
Provably Robust DPO: Aligning Language Models with Noisy Feedback. - Mateusz Gabor, Tomasz Piotrowski, Renato L. G. Cavalcante:
Positive Concave Deep Equilibrium Models. - Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan:
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. - Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams, Johannes Brandstetter:
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics. - Boheng Li, Yishuo Cai, Jisong Cai, Yiming Li, Han Qiu, Run Wang, Tianwei Zhang:
Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation. - Yucen Wang, Shenghua Wan, Le Gan, Shuai Feng, De-Chuan Zhan:
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors. - Yancheng Huang, Kai Yang, Zelin Zhu, Leian Chen:
Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence. - Mingyuan Zhou, Huangjie Zheng, Zhendong Wang, Mingzhang Yin, Hai Huang:
Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation. - Ilya Kaufman, Omri Azencot:
First-Order Manifold Data Augmentation for Regression Learning. - Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components. - Tian Zhu, Milong Ren, Haicang Zhang:
Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints. - André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li:
DE-COP: Detecting Copyrighted Content in Language Models Training Data. - Gergely Neu, Nneka Okolo:
Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization. - Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Guarantees for Regression. - Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, Joel Lehman:
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization. - Juyeon Ko, Inho Kong, Dogyun Park, Hyunwoo J. Kim:
Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis. - Alexander Munteanu, Simon Omlor:
Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation. - Luke Guerdan, Amanda Coston, Ken Holstein, Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. - Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates. - Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster:
EvIL: Evolution Strategies for Generalisable Imitation Learning. - Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht:
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design. - Núria Armengol Urpí, Marco Bagatella, Marin Vlastelica, Georg Martius:
Causal Action Influence Aware Counterfactual Data Augmentation. - Valérie Castin, Pierre Ablin, Gabriel Peyré:
How Smooth Is Attention? - Yi Hu, Xiaojuan Tang, Haotong Yang, Muhan Zhang:
Case-Based or Rule-Based: How Do Transformers Do the Math? - Mohammad-Reza Rahmani, Mohammad Hossein Yassaee, Mohammad Ali Maddah-Ali, Mohammad Reza Aref:
Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints. - Mahdi Nikdan, Soroush Tabesh, Elvir Crncevic, Dan Alistarh:
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation. - Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke:
Estimating Canopy Height at Scale. - Yi Feng, Georgios Piliouras, Xiao Wang:
Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg. - Gaurav Pandey, Yatin Nandwani, Tahira Naseem, Mayank Mishra, Guangxuan Xu, Dinesh Raghu, Sachindra Joshi, Asim Munawar, Ramón Fernandez Astudillo:
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback. - Chanho Park, Namyoon Lee:
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding. - Ron Dorfman, Naseem Yehya, Kfir Yehuda Levy:
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. - Sara Ahmadian, Edith Cohen:
Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs. - Tianshu Chu, Dachuan Xu, Wei Yao, Jin Zhang:
SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity. - Dongyoon Hwang, Byungkun Lee, Hojoon Lee, Hyunseung Kim, Jaegul Choo:
Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control. - Jinho Bok, Weijie J. Su, Jason M. Altschuler:
Shifted Interpolation for Differential Privacy. - Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Dinesh Manocha, Furong Huang, Amrit S. Bedi, Mengdi Wang:
MaxMin-RLHF: Alignment with Diverse Human Preferences. - Junyi Fan, Yuxuan Han, Zijian Liu, Jian-Feng Cai, Yang Wang, Zhengyuan Zhou:
On the Convergence of Projected Bures-Wasserstein Gradient Descent under Euclidean Strong Convexity. - Sikha Pentyala, Mayana Pereira, Martine De Cock:
CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources. - Hyungho Na, Il-Chul Moon:
LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning. - Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann:
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. - Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz, Eric Schulz:
In-Context Learning Agents Are Asymmetric Belief Updaters. - Kirsten Fischer, Javed Lindner, David Dahmen, Zohar Ringel, Michael Krämer, Moritz Helias:
Critical feature learning in deep neural networks. - Steven Wilkins-Reeves, Xu Chen, Qi Ma, Christine Agarwal, Aude Hofleitner:
Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains. - Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho:
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks. - Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka:
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks. - Junwei Yang, Kangjie Zheng, Siyu Long, Zaiqing Nie, Ming Zhang, Xinyu Dai, Wei-Ying Ma, Hao Zhou:
Mol-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective. - Amine Ouasfi, Adnane Boukhayma:
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries. - Hafedh El Ferchichi, Matthieu Lerasle, Vianney Perchet:
Active Ranking and Matchmaking, with Perfect Matchings. - Undral Byambadalai, Tatsushi Oka, Shota Yasui:
Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. - Jingwei Zhang, Cheuk Ting Li, Farzan Farnia:
An Interpretable Evaluation of Entropy-based Novelty of Generative Models. - Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar:
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. - Muthu Chidambaram, Holden Lee, Colin McSwiggen, Semon Rezchikov:
How Flawed Is ECE? An Analysis via Logit Smoothing. - Jinsook Kim:
Can Machines Learn the True Probabilities? - Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. - Zhihai Wang, Jie Wang, Dongsheng Zuo, Yunjie Ji, Xilin Xia, Yuzhe Ma, Jianye Hao, Mingxuan Yuan, Yongdong Zhang, Feng Wu:
A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design. - Youwei Shu, Xi Xiao, Derui Wang, Yuxin Cao, Siji Chen, Jason Xue, Linyi Li, Bo Li:
Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing. - Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost:
FRAPPÉ: A Group Fairness Framework for Post-Processing Everything. - Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, Yu Su:
GPT-4V(ision) is a Generalist Web Agent, if Grounded. - Wenqiang Li, Weijun Li, Lina Yu, Min Wu, Linjun Sun, Jingyi Liu, Yanjie Li, Shu Wei, Yusong Deng, Meilan Hao:
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data. - Sohei Arisaka, Qianxiao Li:
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving. - Youngsoo Jang, Geon-Hyeong Kim, Byoungjip Kim, Yu Jin Kim, Honglak Lee, Moontae Lee:
Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration. - Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi, Masoud Hadi, Arshia Soltani Moakhar, Mohammad Azizmalayeri, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban:
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples. - Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park:
Polynomial-based Self-Attention for Table Representation Learning. - Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yulun Zhang, Radu Timofte:
See More Details: Efficient Image Super-Resolution by Experts Mining. - Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu:
Privacy-Preserving Instructions for Aligning Large Language Models. - Ziyang Zhang, Qizhen Zhang, Jakob Nicolaus Foerster:
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition. - Baohong Li, Haoxuan Li, Ruoxuan Xiong, Anpeng Wu, Fei Wu, Kun Kuang:
Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias. - Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert P. Dick, Ekdeep Singh Lubana, Hidenori Tanaka:
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model. - Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier, Thibaut Vidal:
CF-OPT: Counterfactual Explanations for Structured Prediction. - Shuo Wen, Maria Brbic:
Cross-domain Open-world Discovery. - Yang Zhou, Zijie Zhang, Zeru Zhang, Lingjuan Lyu, Wei-Shinn Ku:
Effective Federated Graph Matching. - Nikita Balabin, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov:
Disentanglement Learning via Topology. - Haoyu Li, Shichang Zhang, Longwen Tang, Mathieu Bauchy, Yizhou Sun:
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks. - Deokjae Lee, Hyun Oh Song, Kyunghyun Cho:
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization. - He Zhang, Chang Liu, Zun Wang, Xinran Wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu:
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction. - Yuguang Yan, Hao Zhou, Zeqin Yang, Weilin Chen, Ruichu Cai, Zhifeng Hao:
Reducing Balancing Error for Causal Inference via Optimal Transport. - Shokichi Takakura, Taiji Suzuki:
Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective. - Amit Peleg, Matthias Hein:
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks. - Sidi Lu, Wenbo Zhao, Chenyang Tao, Arpit Gupta, Shanchan Wu, Tagyoung Chung, Nanyun Peng:
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models. - Md. Musfiqur Rahman, Murat Kocaoglu:
Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference. - Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski:
MOMENT: A Family of Open Time-series Foundation Models. - Jacob Dunefsky, Arman Cohan:
Observable Propagation: Uncovering Feature Vectors in Transformers. - Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong:
Towards Compositionality in Concept Learning. - Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve:
Better & Faster Large Language Models via Multi-token Prediction. - Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song:
S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting. - Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying:
Explaining Graph Neural Networks via Structure-aware Interaction Index. - Brian Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica:
Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters. - Andreas Kontogiannis, Vasilis Pollatos, Sotiris Kanellopoulos, Panayotis Mertikopoulos, Aris Pagourtzis, Ioannis Panageas:
The Computational Complexity of Finding Second-Order Stationary Points. - Thomas Wedenig, Rishub Nagpal, Gaëtan Cassiers, Stefan Mangard, Robert Peharz:
Exact Soft Analytical Side-Channel Attacks using Tractable Circuits. - Chenxin An, Fei Huang, Jun Zhang, Shansan Gong, Xipeng Qiu, Chang Zhou, Lingpeng Kong:
Training-Free Long-Context Scaling of Large Language Models. - Victor Letzelter, David Perera, Cédric Rommel, Mathieu Fontaine, Slim Essid, Gaël Richard, Patrick Pérez:
Winner-takes-all learners are geometry-aware conditional density estimators. - Weijieying Ren, Xiaoting Li, Huiyuan Chen, Vineeth Rakesh, Zhuoyi Wang, Mahashweta Das, Vasant G. Honavar:
TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules. - Thomas F. Burns:
Semantically-correlated memories in a dense associative model. - Keyan Miao, Konstantinos Gatsis:
How Deep Do We Need: Accelerating Training and Inference of Neural ODEs via Control Perspective. - Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
Tandem Transformers for Inference Efficient LLMs. - David Dalton, Dirk Husmeier, Hao Gao:
Physics and Lie symmetry informed Gaussian processes. - Xiner Li, Shurui Gui, Youzhi Luo, Shuiwang Ji:
Graph Structure Extrapolation for Out-of-Distribution Generalization. - Andries P. Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D. Barrett, Arnu Pretorius:
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs. - Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung:
Optimal Transport for Structure Learning Under Missing Data. - Xun Deng, Junlong Liu, Han Zhong, Fuli Feng, Chen Shen, Xiangnan He, Jieping Ye, Zheng Wang:
A3S: A General Active Clustering Method with Pairwise Constraints. - Shihao Zhang, Kenji Kawaguchi, Angela Yao:
Deep Regression Representation Learning with Topology. - Nicolas Michel, Maorong Wang, Ling Xiao, Toshihiko Yamasaki:
Rethinking Momentum Knowledge Distillation in Online Continual Learning. - Yuanhao Pu, Xiaolong Chen, Xu Huang, Jin Chen, Defu Lian, Enhong Chen:
Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation. - Yongmin Lee, Hye Won Chung:
SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching. - Lucas Weber, Ana Busic, Jiamin Zhu:
Reinforcement Learning and Regret Bounds for Admission Control. - Linyuan Gong, Mostafa Elhoushi, Alvin Cheung:
AST-T5: Structure-Aware Pretraining for Code Generation and Understanding. - Guanwen Qiu, Da Kuang, Surbhi Goel:
Complexity Matters: Feature Learning in the Presence of Spurious Correlations. - Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? - Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T. H. Smith, Ramin M. Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, Taiji Suzuki, Michael Poli, Atsushi Yamashita:
State-Free Inference of State-Space Models: The *Transfer Function* Approach. - Junfu Wang, Yuanfang Guo, Liang Yang, Yunhong Wang:
Understanding Heterophily for Graph Neural Networks. - Yichen Wu, Hong Wang, Peilin Zhao, Yefeng Zheng, Ying Wei, Long-Kai Huang:
Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations via Pareto Optimization. - Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen:
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States. - Jaron Maene, Vincent Derkinderen, Luc De Raedt:
On the Hardness of Probabilistic Neurosymbolic Learning. - Pengyu Li, Xiao Li, Yutong Wang, Qing Qu:
Neural Collapse in Multi-label Learning with Pick-all-label Loss. - Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh:
Error Feedback Can Accurately Compress Preconditioners. - Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux:
CARTE: Pretraining and Transfer for Tabular Learning. - Shunxing Fan, Mingming Gong, Kun Zhang:
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data. - Zhiyuan He, Yijun Yang, Pin-Yu Chen, Qiang Xu, Tsung-Yi Ho:
Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning. - Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu:
On The Complexity of First-Order Methods in Stochastic Bilevel Optimization. - Yichen Li, Chicheng Zhang:
Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms. - Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro:
Loss Shaping Constraints for Long-Term Time Series Forecasting. - Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang, Ruifeng Chen, Zhilong Zhang, Xinwei Chen, Yang Yu:
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning. - Xiangxin Zhou, Liang Wang, Yichi Zhou:
Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process. - Francesco Demelas, Joseph Le Roux, Mathieu Lacroix, Axel Parmentier:
Predicting Lagrangian Multipliers for Mixed Integer Linear Programs. - Youngseog Chung, Ian Char, Jeff Schneider:
Sampling-based Multi-dimensional Recalibration. - Antti Koskela, Rachel Redberg, Yu-Xiang Wang:
Privacy Profiles for Private Selection. - Emanuele Loffredo, Mauro Pastore, Simona Cocco, Rémi Monasson:
Restoring balance: principled under/oversampling of data for optimal classification. - Jianliang He, Siyu Chen, Fengzhuo Zhang, Zhuoran Yang:
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems. - Xiangzhe Kong, Wenbing Huang, Yang Liu:
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning. - Joseph Shenouda, Yamin Zhou, Robert D. Nowak:
ReLUs Are Sufficient for Learning Implicit Neural Representations. - Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarathkrishna Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen:
Larimar: Large Language Models with Episodic Memory Control. - Jeremy McMahan, Giovanni Artiglio, Qiaomin Xie:
Roping in Uncertainty: Robustness and Regularization in Markov Games. - Yang Yang, Chao Yang, Boyang Li, Yinghao Fu, Shuang Li:
Neuro-Symbolic Temporal Point Processes. - Defang Chen, Zhenyu Zhou, Can Wang, Chunhua Shen, Siwei Lyu:
On the Trajectory Regularity of ODE-based Diffusion Sampling. - Zhiwei Jia, Vineet Thumuluri, Fangchen Liu, Linghao Chen, Zhiao Huang, Hao Su:
Chain-of-Thought Predictive Control. - Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, Jiawei Zhou:
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding. - Yukinari Hisaki, Isao Ono:
RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning. - Yasen Wang, Junlin Li, Zuogong Yue, Ye Yuan:
An Iterative Min-Min Optimization Method for Sparse Bayesian Learning. - Artyom Gadetsky, Yulun Jiang, Maria Brbic:
Let Go of Your Labels with Unsupervised Transfer. - Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan:
Stability and Multigroup Fairness in Ranking with Uncertain Predictions. - Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu:
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series. - Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman:
Codebook Features: Sparse and Discrete Interpretability for Neural Networks. - Wen-Shu Fan, Su Lu, Xin-Chun Li, De-Chuan Zhan, Le Gan:
Revisit the Essence of Distilling Knowledge through Calibration. - Matej Grcic, Artyom Gadetsky, Maria Brbic:
Fine-grained Classes and How to Find Them. - Cunxiao Du, Jing Jiang, Yuanchen Xu, Jiawei Wu, Sicheng Yu, Yongqi Li, Shenggui Li, Kai Xu, Liqiang Nie, Zhaopeng Tu, Yang You:
GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding. - Ruifeng Chen, Chengxing Jia, Zefang Huang, Tian-Shuo Liu, Xu-Hui Liu, Yang Yu:
Offline Transition Modeling via Contrastive Energy Learning. - Mao Hong, Zhengling Qi, Yanxun Xu:
Model-based Reinforcement Learning for Confounded POMDPs. - Lan Li, Xin-Chun Li, Han-Jia Ye, De-Chuan Zhan:
Enhancing Class-Imbalanced Learning with Pre-Trained Guidance through Class-Conditional Knowledge Distillation. - Lancheng Zou, Wenqian Zhao, Shuo Yin, Chen Bai, Qi Sun, Bei Yu:
BiE: Bi-Exponent Block Floating-Point for Large Language Models Quantization. - Alvaro Labarca, Denis Parra, Rodrigo Toro Icarte:
On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation. - Slimane Thabet, Mehdi Djellabi, Igor Olegovich Sokolov, Sachin Kasture, Louis-Paul Henry, Loïc Henriet:
Quantum Positional Encodings for Graph Neural Networks. - Kartik Sharma, Srijan Kumar, Rakshit Trivedi:
Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation. - Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh:
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities. - Thomas T. C. K. Zhang, Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni:
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples. - Yufei Kuang, Jie Wang, Yuyan Zhou, Xijun Li, Fangzhou Zhu, Jianye Hao, Feng Wu:
Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph. - Jon Schneider, Kiran Vodrahalli:
Online Learning with Bounded Recall. - Caterina Graziani, Tamara Drucks, Fabian Jogl, Monica Bianchini, Franco Scarselli, Thomas Gärtner:
The Expressive Power of Path-Based Graph Neural Networks. - Liangliang Shi, Jack Fan, Junchi Yan:
OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport. - Milad Sefidgaran, Romain Chor, Abdellatif Zaidi, Yijun Wan:
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often! - Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet:
Particle Denoising Diffusion Sampler. - Kang Liu, Yingyi Zhang, Jingyun Zhang, Jinmin Li, Jun Wang, Shaoming Wang, Chun Yuan, Rizen Guo:
DFD: Distilling the Feature Disparity Differently for Detectors. - Huikang Liu, Peng Wang, Longxiu Huang, Qing Qu, Laura Balzano:
Symmetric Matrix Completion with ReLU Sampling. - Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Prompt Sketching for Large Language Models. - Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood:
FlowMM: Generating Materials with Riemannian Flow Matching. - Zijie Geng, Jie Wang, Ziyan Liu, Siyuan Xu, Zhentao Tang, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu:
Reinforcement Learning within Tree Search for Fast Macro Placement. - Bo Li, Wei Wang, Peng Ye:
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy. - Mengchu Xu, Yuxuan Zhang, Jian Wang:
Exponential Spectral Pursuit: An Effective Initialization Method for Sparse Phase Retrieval. - Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh, Russ Tedrake, Huan Zhang:
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation.