ICML Papers
5,975 papers found • Page 107 of 120
QORA: Zero-Shot Transfer via Interpretable Object-Relational Model Learning
Gabriel Stella, Dmitri Loguinov
Q-Probe: A Lightweight Approach to Reward Maximization for Language Models
Kenneth Li, Samy Jelassi, Hugh Zhang et al.
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
Yingru Li, Jiawei Xu, Lei Han et al.
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
Luca Grillotti, Maxence Faldor, Borja G. León et al.
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
Li Ding, Jenny Zhang, Jeff Clune et al.
Quality-Diversity with Limited Resources
Ren-Jian Wang, Ke Xue, Cong Guan et al.
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
Quan Nguyen, Adji Bousso Dieng
Quantum Algorithm for Online Exp-concave Optimization
Jianhao He, Chengchang Liu, Xutong Liu et al.
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
Yexin Zhang, Chenyi Zhang, Cong Fang et al.
Quantum Implicit Neural Representations
Jiaming Zhao, Wenbo Qiao, Peng Zhang et al.
Quantum Positional Encodings for Graph Neural Networks
Slimane Thabet, Mehdi Djellabi, Igor Sokolov et al.
Quantum Theory and Application of Contextual Optimal Transport
Nicola Mariella, Albert Akhriev, Francesco Tacchino et al.
Quasi-Monte Carlo Features for Kernel Approximation
ZHEN HUANG, Jiajin Sun, Yian Huang
QUEST: Query-Aware Sparsity for Efficient Long-Context LLM Inference
Jiaming Tang, Yilong Zhao, Kan Zhu et al.
QuIP$\#$: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
Albert Tseng, Jerry Chee, Qingyao Sun et al.
QuRating: Selecting High-Quality Data for Training Language Models
Alexander Wettig, Aatmik Gupta, Saumya Malik et al.
Q-value Regularized Transformer for Offline Reinforcement Learning
Shengchao Hu, Ziqing Fan, Chaoqin Huang et al.
R2E: Turning any Github Repository into a Programming Agent Environment
Naman Jain, Manish Shetty Molahalli, Tianjun Zhang et al.
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
Sudeep Salgia, Sattar Vakili, Qing Zhao
Random features models: a way to study the success of naive imputation
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut et al.
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet, Ola Ahmad, Audrey Durand
Random Latent Exploration for Deep Reinforcement Learning
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari et al.
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
Jing Xu, Jingzhao Zhang
Random matrix theory improved Fréchet mean of symmetric positive definite matrices
Florent Bouchard, Ammar Mian, Malik TIOMOKO et al.
Random Scaling and Momentum for Non-smooth Non-convex Optimization
Qinzi Zhang, Ashok Cutkosky
Ranking-based Client Imitation Selection for Efficient Federated Learning
Chunlin Tian, Zhan Shi, Xinpeng Qin et al.
RankSEG: A Consistent Ranking-based Framework for Segmentation
Ben Dai, Chunlin Li
Rapid Learning without Catastrophic Forgetting in the Morris Water Maze
Raymond L Wang, Jaedong Hwang, Akhilan Boopathy et al.
Rate-Optimal Policy Optimization for Linear Markov Decision Processes
Uri Sherman, Alon Cohen, Tomer Koren et al.
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation
Jiawei Zhou, Linye Lyu, Daojing He et al.
Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
Jian Liang, Sheng, Zhengbo Wang et al.
Reason for Future, Act for Now: A Principled Architecture for Autonomous LLM Agents
Zhihan Liu, Hao Hu, Shenao Zhang et al.
Receptive Fields As Experts in Convolutional Neural Architectures
Dongze Lian, Weihao Yu, Xinchao Wang
ReconBoost: Boosting Can Achieve Modality Reconcilement
Cong Hua, Qianqian Xu, Shilong Bao et al.
Recovering Labels from Local Updates in Federated Learning
Huancheng Chen, Haris Vikalo
Recovering the Pre-Fine-Tuning Weights of Generative Models
Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen
Recurrent Distance Filtering for Graph Representation Learning
Yuhui Ding, Antonio Orvieto, Bobby He et al.
Recurrent Early Exits for Federated Learning with Heterogeneous Clients
Royson Lee, Javier Fernandez-Marques, Xu Hu et al.
ReDiffuser: Reliable Decision-Making Using a Diffuser with Confidence Estimation
Nantian He, Shaohui Li, Zhi Li et al.
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Yufei Huang, Odin Zhang, Lirong Wu et al.
Reducing Balancing Error for Causal Inference via Optimal Transport
Yuguang Yan, Hao Zhou, Zeqin Yang et al.
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Yuchen Yang, Yingdong Shi, Cheems Wang et al.
Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation
Weiming Liu, Xiaolin Zheng, Chaochao Chen et al.
Reducing sequential change detection to sequential estimation
Shubhanshu Shekhar, Aaditya Ramdas
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion
Xuantong Liu, Tianyang Hu, Wenjia Wang et al.
Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations
Ze Cheng, Zhongkai Hao, Wang Xiaoqiang et al.
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
Xiaobo Xia, Jiale Liu, Shaokun Zhang et al.
Refining Minimax Regret for Unsupervised Environment Design
Michael Beukman, Samuel Coward, Michael Matthews et al.
Reflected Flow Matching
Tianyu Xie, Yu Zhu, Longlin Yu et al.
Reflective Policy Optimization
Yaozhong Gan, yan renye, zhe wu et al.