ICML

2022–2026 LIVE

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Machine Learning

2026 · 6,341 papers

このEditionに反映済みのmain full research papersのみ掲載しています。著者リンクは日本の大学への著者別所属根拠がある場合に限ります。

2026 · ICML
Provable Training Data Identification for Large Language Models
Zhenlong Liu, Hao Zeng, Weiran Huang, Hongxin Wei
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Provably Convergent Actor-Critic in Risk-averse MARL
Yizhou Zhang, Eric Mazumdar
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
QUOC TUNG LE, Anh Nguyen, Viet Anh Nguyen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
QUOC TUNG LE, Anh Nguyen, Viet Anh Nguyen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning
Tian Xu, Zexuan Chen, Zhilong Zhang, Yi-Chen Li, Chenyang Wang, lei yuan, Yang Yu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Provably Label-Efficient Conformal Prediction
Andrew Ilyas, Joonhyuk Ko, Jingwu Tang, Steven Wu, Jiahao Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Provably Learning Attention with Queries
Satwik Bhattamishra, Kulin Shah, Michael Hahn, Varun Kanade
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Provably Protecting Fine-Tuned LLMs from Training Data Extraction while Preserving Utility
Tom Segal, Yuval Elovici, Asaf Shabtai
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Proximal Splitting Methods for Hybrid Differentiable Models
Abdel-Rahim Mezidi, Jordan Patracone, Amaury Habrard
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Proximal-Based Generative Modeling for Bayesian Inverse Problems
Boyang Zhang, Zhiguo Wang, Ya-Feng Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis–Hastings with Approximate Operators
Youguang Chen, George Biros
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Proxy Compression for Language Modeling
Lin Zheng, Li Xinyu, Qian Liu, Xiachong Feng, Lingpeng Kong
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Pruning at Initialisation through the lens of Graphon Limit: Convergence, Expressivity, and Generalisation
Hoang Pham, The-Anh Ta, Long Tran-Thanh
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Pseudo-Mallows for Efficient Probabilistic Preference Learning
Sylvia Liu, Valeria Vitelli, Carlo Mannino, Arnoldo Frigessi, Ida Scheel
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
PsumQuant: In-line Post-training Partial Sum Quantizer for Energy Efficient NPU Inference
Sangwoo Hwang, Yeeun Hong, Jaeha Kung
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Pull Requests as a Training Signal for Repo-Level Code Editing
Qinglin Zhu, Tianyu Chen, Shuai Lu, Lei Ji, Runcong Zhao, Murong Ma, Xiangxiang Dai, Yulan He, Lin Gui, Peng CHENG, Yeyun Gong
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Push, Pop, Parallelize: Stack-Augmented Linear Attention via the Delta Rule
Anh T Nguyen, Saleh Momeni, Ashutosh Chaubey, Changnan Xiao, Bing Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
Yihang Yao, Zhepeng Cen, Haohong Lin, Shiqi Liu, Zuxin Liu, Jiacheng Zhu, Zhang-Wei Hong, Laixi Shi, Ding Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
Chuxue Cao, Jinluan Yang, Haoran Li, Kunhao Pan, Zijian Zhao, Zhengyu Chen, Yuchen Tian, Lijun Wu, Conghui He, Yike Guo, Sirui Han
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Pushing the Limits of Block Rotations in Post-Training Quantization
Sai Sanjeet, Ian Colbert, Pablo Monteagudo-Lago, Giuseppe Franco, Yaman Umuroglu, Nicholas Fraser
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference
Yushu Zhao, Zheng Wang, Minjia Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning
John Wu, Yongda Fan, Zhenbang Wu, Paul Landes, Eric Schrock, Sayeed Sajjad Razin, Arjun Chatterjee, Naveen Baskaran, Joshua Steier, Andrea Fitzpatrick, Bilal Arif, Rian Atri, Jathurshan Pradeepkumar, Siddhartha Laghuvarapu, Junyi Gao, Adam Cross, Jimeng Sun
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
PyVision-RL: Forging Open Agentic Vision Models via RL
Shitian Zhao, Shaoheng Lin, Ming Li, Haoquan Zhang, Wenshuo Peng, Kaipeng Zhang, Chen Wei
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Q-CLIP: Unleashing the Power of Vision-Language Models for Video Quality Assessment through Unified Cross-Modal Adaptation
Yachun Mi, Yu Li, Yanting Li, Chen Hui, Tong Zhang, Zhixuan Li, Chenyue Song, Wei Yang Bryan Lim, Shaohui Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Q-Delta: Beyond Key–Value Associative State Evolution
Sumin Park, Seojin Kim, Noseong Park
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution
Xun Zhang, Kaicheng Yang, Hongliang Lu, Haotong Qin, Yong Guo, Yulun Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Q-Flow: Stable and Expressive Reinforcement Learning with Flow-based Policy
JaeHyeok Doo, Byeongguk Jeon, Seonghyeon Ye, Kimin Lee, Minjoon Seo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Q-SAM: Unlocking Sharpness-Aware Minimization for Generalization in Offline Reinforcement Learning
Da Wang, Yi Ma, Ting Guo, Lin Li, Wei Wei, Jiye Liang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
Natalia Frumkin, Diana Marculescu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Q-Tab: Quantized Tabular Data Generator
Julian Wustl, Philipp Haid, Yarema Okhrin, Claudius Schnörr
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QEDBench: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs
Santiago Gonzalez, Alireza Amiribavandpour, Peter Ye, Edward Zhang, Ruslans Aleksejevs, Todor Antić, Polina Baron, Sujeet Bhalerao, Shubhrajit Bhattacharya, Zachary Burton, John Byrne, Hyungjun Choi, Nujhat Ahmed Disha, Koppány I Encz, Yuchen Fang, Robert Joseph George, Ebrahim Ghorbani, Alan Goldfarb, Jing Guo, Meghal Gupta, Stefano Huber, Annika Kanckos, Minjung Kang, Hyun Jong Kim, Dino Lorenzini, Levi Lorenzo, Tianyi Mao, Giovanni Marzenta, Ariane Masuda, Lukas Mauth, Ana Mickovic, Andrés Miniguano-Trujillo, Antoine Moulin, Wenqi Ni, Tomos Parry, Kevin Ren, Hossein Roodbarani, Mathieu Rundström, Manjil Saikia, Detchat Samart, Rebecca Steiner, Connor Stewart, Dhara Thakkar, Jeffrey Tse, Vasiliki Velona, Yunhai Xiang, Sibel Yalçın, Jun Yan, Ji Zeng, Arman Cohan, Quanquan Liu
Machine Learning
日本大学: 名古屋大学
2026 · ICML
QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
Xing Lei, Jincheng Wang, Xuetao Zhang, Donglin Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QPKO: Differentiable QP-Embedded Deep Koopman Framework for Modeling Nonlinear Systems
Runze Tian, Peng Kou
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QPoint: End-to-End Lightweight Point Cloud Processing via Robust Quaternion Feature Learning
Zhouzhiming Zhou, Yong He, Chaoxu Mu, Qiaoyun Wu, Ajmal Mian
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
Kanghyun Noh, Jinheon Choi, Yulhwa Kim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QUATRO: Query-Adaptive Trust Region Policy Optimization for LLM Fine-tuning
Doyeon Lee, Eunyi Lyou, Hyunsoo Cho, Soo Kyung Kim, Joonseok Lee, Jaemoo Choi
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QiMeng-ChipV-RTL: Exploiting Information Locality for IP-level Verilog Generation
Hanqi Lyu, Di Huang, Yaoyu Zhu, Kangcheng Liu, Bohan Dou, Chongxiao Li, Pengwei Jin, Shuyao Cheng, Rui Zhang, Zidong Du, Qi Guo, Xing Hu, Yunji Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QiMeng-LibBench: Benchmarking LLM Agents for Library-Scale Cross-Architecture Migration
Weijia Li, KE GAO, Jiajie Li, Han Sun, Yuhe Ding, Yongdong Mai, Yiran Le, Yongjie Qian, Zhibin Zhang, Xinyu Wang, Limin Cheng, Shouxu Kuang, Pengfei Chen, Ling Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QiMeng-PerceptOS: Semantic-Aware Kernel Optimization for OS-Intensive Workloads via Hardware-Software Alignment
Huilai Chen, Yuanbo Wen, Liangfeng Li, Shaohui Peng, Jingzhe Zhu, Jun Bi, Xuzhi Zhang, Qi Guo, Ling Li, Yunji Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
Shvetank Prakash, Andrew Cheng, Mark Mazumder, Arya Tschand, Varun Gohil, Jeffrey Ma, Jason Yik, Zishen Wan, Jessica A. Quaye, Elisavet Alvanaki, Avinash Kumar, Chandrashis Mazumdar, Tuhin Khare, Alexander Ingare, Ikechukwu Uchendu, Radhika Ghosal, Abhishek Tyagi, Chenyu Wang, Andrea Mattia Garavagno, Sarah Gu, Alice Guo, Grace Hur, Luca Carloni, Tushar Krishna, Ankita Nayak, Amir Yazdanbakhsh, Vijay Janapa Reddi
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QuITE: Query-Based Irregular Time Series Embedding
Junghoon Lim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quadratically Regularized Optimal Transport: Localization Bounds and Affine Case Analysis
Long Nguyen-Chi, Nam Nguyen, Binh T. Nguyen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
Haocheng Xi, Shuo Yang, Yilong Zhao, Muyang Li, Han Cai, Xingyang Li, Yujun Lin, Zhuoyang Zhang, Jintao Zhang, Xiuyu Li, Zhiying Xu, Jun Wu, Chenfeng Xu, Ion Stoica, Song Han, Kurt Keutzer
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QuantWear: Quantum-scale Wear Particle Detection for Jet Engine Diagnosis
Zheng Wang, Yanwen Wang, Tianyu Fang, Jiaxing Shen, Yisen Kang, Di Wu, Yuanqing Zheng
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying Biases in LLM-as-a-Judge Evaluations
Magda Dubois, Harry Coppock, Mario Giulianelli, Ole Jorgensen, Timo Flesch, Lennart Luettgau, Cozmin Ududec
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying Cross-Domain Knowledge Distillation in the Presence of Domain Shift
Xiangchao Li, Xiao Han, Qing Yang, Xin Tong
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying Error Propagation and Model Collapse in Diffusion Models
Naïl B. Khelifa, Richard E Turner, Ramji Venkataramanan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying Frontier LLM Capabilities for Container Sandbox Escape
Rahul Marchand, Art Cathain, Jerome Wynne, Philippos Giavridis, Sam Deverett, John Wilkinson, Jason Gwartz, Harry Coppock
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying LLM Attention-Head Stability: Implications for Circuit Universality
Karan Bali, Jack Stanley, Praneet Suresh, Danilo Bzdok
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying and Optimizing Simplicity via Polynomial Representations
Tianren Zhang, Xiangxin Li, Minghao Xiao, Guanyu Chen, Feng Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying the Generalization Gap in Seizure Detection: A Large-Scale Empirical Benchmark via the SzCORE Challenge
Jonathan Dan, Amirhossein Shahbazinia, Christodoulos Kechris, David Atienza
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying the Salience of Geo-Cultural Values for Pluralistic Safety Alignment
Arkadiy Saakyan, Charvi Rastogi, Lora Aroyo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles
Mehmet Ozgur Turkoglu, Dominik J. Mühlematter, Alexander Becker, Konrad Schindler, Helge Aasen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantifying the noise sensitivity of the Wasserstein metric for images
Erik Lager, Gilles Mordant, Amit Moscovich
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantile-Free Uncertainty Quantification in Graph Neural Networks
Soyoung Park, Hwanjun Song, Sungsu Lim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning
Lin-Han Jia, Siyu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantum Algorithms for Triangle Cut Sparsification
Shan Jiang, Pan Peng
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantum Robust Inner Minimization for Reinforcement Learning with Quadratic Speed-Up in Query Complexity
Hyun Kyu Lee, Joongheon Kim, Sung Whan Yoon
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quantum latent distributions in deep generative models
Omar Bacarreza, Thorin Farnsworth, Alexander Makarovskiy, Hugo Wallner, Tessa Hicks, Santiago Sempere-Llagostera, John Price, Robert Francis-Jones, William Clements
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
QuantumBoost: A lazy, yet fast, quantum algorithm for learning with weak hypotheses
Amira Abbas, Yanlin Chen, Tuyen Nguyen, Ronald de Wolf
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation
Andrei Panferov, Erik Schultheis, Soroush Tabesh, Dan Alistarh
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Quaternion Self-Attention with Shared Scores
Shogo Yamauchi, Tohru Nitta, Hideaki Tamori
Machine Learning
日本大学: 東京女子大学
2026 · ICML
Query Circuits: Explaining How Language Models Answer User Prompts
Tung-Yu Wu, Fazl Barez
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
Hwiyeong Lee, Ingyu Bang, Uiji Hwang, Hyelim Lim, Taeuk Kim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Query-Based Asymmetric Modeling with Decoupled Input–Output Rates for Speech Restoration
Ui-Hyeop Shin, Jaehyun Ko, Woocheol Jeong, Hyung-Min Park
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Query-efficient model evaluation using cached responses
Hayden Helm, Ben Johnson, Carey Priebe
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better
Yizhou Min, Yizhou Lu, Lanqi Li, Zhen Zhang, Jiaye Teng
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
R$^3$L: Reasoning 3D Layouts from Relative Spatial Relations
Zhifeng Gu, Yuqi Wang, Bing WANG
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
R-Diverse: Mitigating Diversity Illusion in Self-Play LLM Training
Gengsheng Li, Jinghan He, Shijie Wang, Ruiqi Liu, Renrui Zhang, Zijun Yao, Junfeng Fang, Haiyun Guo, Dan Zhang, Jinqiao Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?
Jingyi Zhang, Tianyi Lin, Huanjin Yao, Xiang Lan, Shunyu Liu, Jiaxing Huang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
R2-Router: A New Paradigm for LLM Routing with Reasoning
Jiaqi Xue, Qian Lou, Jiarong Xing, Heng Huang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
Sanghyeob Song, Donghyeok Lee, Jinsik Kim, Sungroh Yoon
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry
Xinchang Wang, Yunhao Chen, Yuechen Zhang, Congcong Bian, Zihao Guo, Xingjun Ma, Hui Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation
Sanghwan Jang, Minjin Jeon, Minsoo Kim, Seong Jin Choi, Dongha Kim, Hwanjo Yu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
Sai Hao, Hao Zeng, Hongxin Wei, Bingyi Jing
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAD: Retrieval High-quality Demonstrations to Enhance Decision-making
Lu Guo, Yixiang Shan, Zhengbang Zhu, Qifan Liang, Lichang Song, Ting Long, Weinan Zhang, Yi Chang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RADAR: Defending RAG Dynamically against Retrieval Corruption
Ziyuan Chen, Yueming Lyu, Yi Liu, Weixiang Han, JING DONG, Caifeng Shan, Tieniu Tan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
Zhen Zhang, Wanjing Zhou, Juncheng Li, Hao Fei, Jun Wen, Wei Ji
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RADE: Random Add-Drop Edge as a Regularizer
Danial Saber, Amirali Salehi-Abari
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RADIO1D: Elastic Representations for Condensed Vision Modeling
Greg Heinrich, Mike Ranzinger, Collin McCarthy, Natan Bagrov, Eugene Khvedchenya, Bryan Catanzaro, Jan Kautz, Andrew Tao, Pavlo Molchanov
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAG without Forgetting: Continual Query-Infused Key Memory
Yuntong Hu, Sha Li, Naren Ramakrishnan, Liang Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAGEN-2: Reasoning Collapse in Agentic RL
Zihan (Zenus) Wang, Chi Gui, Xing Jin, Qineng Wang, Licheng Liu, Kangrui Wang, Shiqi Chen, Linjie Li, Zhengyuan Yang, Pingyue Zhang, Yiping Lu, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, Manling Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAIGen: Rare Attribute Identification in Text-to-Image Generative Models
Silpa Vadakkeeveetil Sreelatha, Dan Wang, Serge Belongie, Muhammad Awais, Anjan Dutta
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
Kai Fukazawa, Kunal Mundada, Iman Soltani
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections
Yali Fink, Ido Ben-Yair, Lars Ruthotto, Eran Treister
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
Yuhan Tang, Kangxin Cui, Jung Ho Park, Yibo Zhao, Xuan Jiang, Haoze He, Jiangbo Yu, Haris Koutsopoulos, Jinhua Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RAT+: Train Dense, Infer Sparse - Recurrence Augmented Attention for Dilated Inference
Xiuying Wei, Caglar Gulcehre
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RBCBF: Decoding Time Safety Alignment via Risk Guided Rollback and Barrier Control
Tianxiang Chen, Jingyuan Zhou, Longhao Yan, Kaidi Yang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RC-FCL: Combating Asynchronous Concept Drift in Federated Continual Learning via Retrospective Calibration
Hang Su, Yijun Mo, Zhiyu Zhang, Yankai Jiang, Bo Liu, Yichen Li, Imran Razzak
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization
LIU SONGMING, Bangguo Li, Kai Ma, Lingxuan Wu, Hengkai Tan, Xiao Ouyang, Hang Su, Jun Zhu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents
jialiang zhu, Gongrui Zhang, Xiaolong Ma, Lin Xu, Miaosen Zhang, Ruiqi Yang, Song Wang, Kai Qiu, Zhirong Wu, Qi Dai, Ruichun Ma, Bei Liu, Yifan Yang, Chong Luo, Zhengyuan Yang, Linjie Li, Lijuan Wang, Weizhu Chen, Xin Geng, Baining Guo
Machine Learning
日本大学: 早稲田大学
2026 · ICML
REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge
Yasi Zhang, Tianyu Chen, Mingyuan Zhou, Oscar Leong, Ying Nian Wu, Michal Lukasik
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment
Kai Ye, Xianwei Mao, Sheng Zhou, Zirui Shao, Ye Mo, Liangliang Liu, Haikuan Huang, Bin Li, Jiajun Bu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
Buyun Liang, Jinqi Luo, Liangzu Peng, Kwan Ho Ryan Chan, Darshan Thaker, Kaleab Kinfu, Fengrui Tian, Hamed Hassani, Rene Vidal
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
REAR: Test-time Preference Realignment through Reward Decomposition
Fuxiang Zhang, Pengcheng Wang, Chenran Li, Yi-Chen Li, Yuxin Chen, Lang Feng, Chenfeng Xu, Masayoshi Tomizuka, Bo An
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data
Xuan Zhao, Lena Krieger, Zhuo Cao, Arya Bangun, Hanno Scharr, Ira Assent
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RECOVER: Reliable Detection of Unauthorized Data Usage in Text-to-Image Diffusion Models via Inversion Robustness
Yanhao Wei, Xiaokang Zhao, Boheng Li, Yang Zhang, Run Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning
Jinhan Liu, Mahsa Shoaran
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
RED-HDP-HMM: Observation-Dependent Durations for Bayesian Nonparametric Sequential Models
Mikołaj Słupiński, Piotr Lipinski
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
REG: In-Sample RL via Regularizing the Evaluation Gap
Hanpu Shen, Weining Shen, Roy Fox
Machine Learning
日本大学: 関与なし（対象大学）