Conferences · eligible papers

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。

  1. 2026 · ICML

    Recognize Your Orchestrator: An Entropy Dynamics Perspective for LLM Multi-Agent Systems

    Junze Zhu, Weihao Chen, Xuanwang Zhang, Zhen Wu, Xinyu Dai

    Machine Learning

    日本大学: 関与なし(対象大学)

  2. 2026 · ICML

    Reconstructing Template-Memorized Images from Natural Prompts

    Sol Yarkoni, Mahmood Sharif, Roi Livni

    Machine Learning

    日本大学: 関与なし(対象大学)

  3. 2026 · ICML

    Reconstruction Outcomes Look Similar but Processes Differ: Improving Context Consistency and Coverage in Graph Masked Auto-Encoder

    Geng Tang, Keyu Liu, Xibei Yang, Yuhua Qian

    Machine Learning

    日本大学: 関与なし(対象大学)

  4. 2026 · ICML

    Recontextualization Mitigates Specification Gaming Without Modifying the Specification

    Ariana Azarbal, Victor Gillioz, Vladimir Ivanov, Bryce Woodworth, jacob drori, Nevan Wichers, Aram Ebtekar, Alex Cloud, Alexander Turner

    Machine Learning

    日本大学: 関与なし(対象大学)

  5. 2026 · ICML

    Recovering Hidden Reward in Diffusion-Based Policies

    Yanbiao Ji, Qiuchang Li, Yuting Hu, Shaokai Wu, Wenyuan XIE, Guodong ZHANG, Qichen He, Deyi Ji, Yue Ding, Hongtao Lu

    Machine Learning

    日本大学: 関与なし(対象大学)

  6. 2026 · ICML

    Recovering Policy-Induced Errors: Benchmarking and Trajectory Synthesis for Robust GUI Agents

    Tianpeng Bu, Xin Liu, Qihua Chen, Hao Jiang, Shurui Li, hongtao duan, Lu Jiang, lulu hu, Bin Yang, Minying Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  7. 2026 · ICML

    Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations

    Yilun Kuang, Yash Dagade, Tim G. J. Rudner, Randall Balestriero, Yann LeCun

    Machine Learning

    日本大学: 関与なし(対象大学)

  8. 2026 · ICML

    Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data

    Stefanos Pertigkiozoglou, Mircea Petrache, Shubhendu Trivedi, Kostas Daniilidis

    Machine Learning

    日本大学: 関与なし(対象大学)

  9. 2026 · ICML

    Recurrent Structural Policy Gradient for Partially Observable Mean Field Games

    Clarisse Wibault, Sebastian Towers, Tiphaine Wibault, Juan Duque, Johannes Forkel, George Whittle, Andreas Schaab, Chiyuan Wang, Yucheng Yang, Michael A Osborne, Benjamin Moll, Jakob Foerster

    Machine Learning

    日本大学: 関与なし(対象大学)

  10. 2026 · ICML

    Recursive Binding on a Budget: Subspace Carving in Order-$p$ Tensor Memories

    Travis Pence, Daisuke Yamada, Vikas Singh

    Machine Learning

    日本大学: 関与なし(対象大学)

  11. 2026 · ICML

    Recursive Models for Long-Horizon Reasoning

    Chenxiao Yang, Nati Srebro, Zhiyuan Li

    Machine Learning

    日本大学: 関与なし(対象大学)

  12. 2026 · ICML

    Recursive Monte-Carlo Tree Search

    Benjamin Howard, Keith Frankston

    Machine Learning

    日本大学: 関与なし(対象大学)

  13. 2026 · ICML

    RedDebate: Safer Responses Through Multi-Agent Red Teaming Debates

    Ali Asad, Stephen Obadinma, Radin Shayanfar, Xiaodan Zhu

    Machine Learning

    日本大学: 関与なし(対象大学)

  14. 2026 · ICML

    RedVisor: Reasoning-Aware Prompt Injection Defense via Zero-Copy KV Cache Reuse

    Mingrui Liu, Sixiao Zhang, Cheng Long, Kwok Yan Lam

    Machine Learning

    日本大学: 関与なし(対象大学)

  15. 2026 · ICML

    Reducing Per-Sample Harm in Stochastic Optimization

    Apostolos Avranas

    Machine Learning

    日本大学: 関与なし(対象大学)

  16. 2026 · ICML

    Reduction of Probabilistic Chemical Reaction Networks

    Mauricio Montes, Grégoire Sergeant-Perthuis

    Machine Learning

    日本大学: 関与なし(対象大学)

  17. 2026 · ICML

    RefChess: Training-Free Contextual Search for Zero-Shot Referring Image Segmentation

    Shiyan Tong, Jinxia Zhang, Zhiyuan Wang, Hao Tian, YingYing Wang, Kanjian Zhang, Haikun Wei

    Machine Learning

    日本大学: 関与なし(対象大学)

  18. 2026 · ICML

    Reference-Free Meta-Learning for Generalized Implicit Neural Representation in Efficient MRI Reconstruction

    Haonan Zhang, Qing Wu, Xuanyu Tian, Bowen Li, Yuyao Zhang, Hongjiang Wei

    Machine Learning

    日本大学: 関与なし(対象大学)

  19. 2026 · ICML

    Referring Multiple Regions with Large Multimodal Models via Contextual Latent Steering

    Yun Xing, Hanyuan Liu, Jiahao Nie, Shijian Lu

    Machine Learning

    日本大学: 関与なし(対象大学)

  20. 2026 · ICML

    RefineEvo: Planning-Guided Heuristic Evolution with Bidirectional Experience

    Yang Wu, Junran Pan, Yifan Zhang, Ning Xu, Fanshuo Zeng, Jian Cheng

    Machine Learning

    日本大学: 関与なし(対象大学)

  21. 2026 · ICML

    Refined Analysis of Entropy-Regularized Actor-Critic

    Safwan Labbi, Paul Mangold, Daniil Tiapkin, Eric Moulines

    Machine Learning

    日本大学: 関与なし(対象大学)

  22. 2026 · ICML

    Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion

    Chunming He, Rihan Zhang, Fengyang Xiao, Dingming Zhang, Zhiwen Cao, Sina Farsiu

    Machine Learning

    日本大学: 関与なし(対象大学)

  23. 2026 · ICML

    Refining Dual Spectral Sparsity in Transformed Tensor Singular Values

    Andong Wang, Yuning Qiu, Haonan Huang, Zhong Jin, Guoxu Zhou, Qibin Zhao

    Machine Learning

    日本大学: 関与なし(対象大学)

  24. 2026 · ICML

    ReflFlow: Learning Geometry-Guided Ray Tracing for Dynamic Specular Reconstruction

    Jiachen Tao, Junyi Wu, Haoxuan Wang, Zongxin Yang, Dawen Cai, Yan Yan

    Machine Learning

    日本大学: 関与なし(対象大学)

  25. 2026 · ICML

    Reflect-then-Correct: Rebalancing Task Optimization for Generalizable Meta-Reinforcement Learning via Distributional Value Error Reduction

    Min Wang, Xin Li, Ye He, Mingzhong Wang, Yonggang Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  26. 2026 · ICML

    Reflective Hamiltonian Monte Carlo: Mixing Analysis and Application to Sampling on Stiefel Manifold

    Kwangmin Lee, Yeonhee Park, Sewon Park

    Machine Learning

    日本大学: 関与なし(対象大学)

  27. 2026 · ICML

    Reflector: Internalizing Step-wise Reflection against Indirect Jailbreaks

    Jiachen Ma, Jiawen Zhang, Xiangtian Li, Bo Zou, Chaochao Lu, Chao Yang

    Machine Learning

    日本大学: 関与なし(対象大学)

  28. 2026 · ICML

    Reflex: Real-Time Vision-Language-Action Control through Streaming Inference

    Yuanchun Guo, Bingyan Liu

    Machine Learning

    日本大学: 関与なし(対象大学)

  29. 2026 · ICML

    Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes

    Yan Zhang, Xuefeng Liu, Sipeng Chen, Sascha Ranftl, Chong Liu, Shibo Li

    Machine Learning

    日本大学: 関与なし(対象大学)

  30. 2026 · ICML

    Regression Language Models for Code

    Yash Akhauri, Xingyou Song, Arissa Wongpanich, Bryan Lewandowski, Mohamed Abdelfattah

    Machine Learning

    日本大学: 関与なし(対象大学)

  31. 2026 · ICML

    Regret Minimization With a Crowd of Awakening Experts

    Anna Lunghi, Gianmarco Genalti, Alberto Marchesi, Matteo Castiglioni

    Machine Learning

    日本大学: 関与なし(対象大学)

  32. 2026 · ICML

    Regret Pre-training: Bridging Prior and Posterior Views for Enhanced Knowledge Grounding

    Mingkuan Zhao, Xiayu Sun, Wentao Hu, Suquan Chen, Jiaxuan Li, Xiaoyan Zhu, Xin Lai, Jiayin Wang

    Machine Learning

    日本大学: 関与なし(対象大学)

  33. 2026 · ICML

    Regret-Based Federated Causal Discovery with Unknown Interventions

    Federico Baldo, Charles Assaad

    Machine Learning

    日本大学: 関与なし(対象大学)

  34. 2026 · ICML

    Regularized Discriminative Alignment for Deep Representations under Label Shift

    Hengchao Shi, Boen Jiang, Guanhua Fang, Wen Yu, Ming Zheng

    Machine Learning

    日本大学: 関与なし(対象大学)

  35. 2026 · ICML

    Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief

    Hongqiang Lin, Pengfei Wang, Nenggan Zheng

    Machine Learning

    日本大学: 関与なし(対象大学)

  36. 2026 · ICML

    Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis

    Tianwei Lin, Zhongwei Qiu, Jie Cao, Jiang Liu, Wenjie Yan, Bo Zhang, Yu Zhong, Wenqiao Zhang, Yingda Xia, Ling Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  37. 2026 · ICML

    Reinforced Sequential Monte Carlo for Amortised Sampling

    Sanghyeok Choi, Sarthak Mittal, Víctor Elvira, Jinkyoo Park, Esmeralda S. Whitammer

    Machine Learning

    日本大学: 関与なし(対象大学)

  38. 2026 · ICML

    Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training

    Song Lai, Haohan Zhao, Rong Feng, Changyi Ma, Wenzhuo Liu, Hongbo Zhao, Xi Lin, Dong Yi, Qingfu Zhang, Hongbin Liu, Gaofeng Meng, Fei Zhu

    Machine Learning

    日本大学: 関与なし(対象大学)

  39. 2026 · ICML

    Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality

    Amogh Palasamudram, Jakub Svoboda, Suguman Bansal, Krishnendu Chatterjee

    Machine Learning

    日本大学: 関与なし(対象大学)

  40. 2026 · ICML

    Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)

    Marius Knorr, Robert Müller, Jan Bremer, Nils Schweingruber

    Machine Learning

    日本大学: 関与なし(対象大学)

  41. 2026 · ICML

    Reinforcement Learning via Self-Distillation

    Jonas Hübotter, Frederike Lübeck, Lejs Behric, Anton Baumann, Marco Bagatella, Daniel Marta, Ido Hakimi, Idan Shenfeld, Thomas Kleine Buening, Carlos Guestrin, Andreas Krause

    Machine Learning

    日本大学: 関与なし(対象大学)

  42. 2026 · ICML

    Reinforcement Learning with Action-Triggered Observations

    Alexander Ryabchenko, Wenlong Mou

    Machine Learning

    日本大学: 関与なし(対象大学)

  43. 2026 · ICML

    Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces

    Haitong Ma, Ofir Nabati, Aviv Rosenberg, Bo Dai, Oran Lang, Craig Boutilier, Na Li, Shie Mannor, Lior Shani, Guy Tennenholtz

    Machine Learning

    日本大学: 関与なし(対象大学)

  44. 2026 · ICML

    Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems

    Jonathan Colaco Carr, Prakash Panangaden, Doina Precup, Benjamin Van Roy

    Machine Learning

    日本大学: 関与なし(対象大学)

  45. 2026 · ICML

    Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification

    Youssef Mroueh

    Machine Learning

    日本大学: 関与なし(対象大学)

  46. 2026 · ICML

    Reinforcement-aware Knowledge Distillation for LLM Reasoning

    zhaoyang zhang, Shuli Jiang, Yantao Shen, Yuting Zhang, Dhananjay Ram, Shuo Yang, Zhuowen Tu, Wei Xia, Stefano Soatto

    Machine Learning

    日本大学: 関与なし(対象大学)

  47. 2026 · ICML

    Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue

    Ning Gao, Wei Zhang, Yuqin Dai, Ling Shi, Ziyin Wang, Yujie Wang, Wei He, Jinpeng Wang, Chaozheng Wang

    Machine Learning

    日本大学: 関与なし(対象大学)

  48. 2026 · ICML

    Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases

    Jun Yin, Peng Huo, Bangguo Zhu, Hao Yan, Xuchen Wang, Shirui Pan, Chengqi Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  49. 2026 · ICML

    Relational In-Context Learning via Synthetic Pre-training with Structural Prior

    Yanbo Wang, Jiaxuan You, Chuan Shi, Muhan Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  50. 2026 · ICML

    Relational Structural Causal Models

    Adiba Ejaz, Elias Bareinboim

    Machine Learning

    日本大学: 関与なし(対象大学)

  51. 2026 · ICML

    Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference

    CHAO WANG, Luca Nepote, Giulio Franzese, Pietro Michiardi

    Machine Learning

    日本大学: 関与なし(対象大学)

  52. 2026 · ICML

    RelaxFlow: Text-Driven Amodal 3D Generation

    Jiayin Zhu, Guoji Fu, Xiaolu Liu, Qiyuan He, Yicong Li, Angela Yao

    Machine Learning

    日本大学: 関与なし(対象大学)

  53. 2026 · ICML

    RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse

    Yingsheng Geng, Yuchong Gao, Weihong Wu, Guyue Liu, Jiang liu

    Machine Learning

    日本大学: 関与なし(対象大学)

  54. 2026 · ICML

    Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls

    Kirill Shevkunov, Andrey Ploskonosov, Liudmila Prokhorenkova

    Machine Learning

    日本大学: 関与なし(対象大学)

  55. 2026 · ICML

    Reliability-Aware LLM Alignment from Inconsistent Human Feedback

    Jingyi Huang, Ruohan Zong, Yujun Feng, Liran Ma, Lanyu Shang, Yang Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  56. 2026 · ICML

    Reliable Confidence Alignment for Generalized Category Discovery

    Jiawei Yu, Zijian Gao, Tianjiao Wan, Xuan Liu, Cheng Yang, Kele Xu

    Machine Learning

    日本大学: 関与なし(対象大学)

  57. 2026 · ICML

    Reliable Neighborhood-Aware Multi-View Outlier Detection

    Huijie Ma, Haoyuan Xin, Lei Meng, Guanzhou Ke, Yongyong Chen, Guoqing Chao

    Machine Learning

    日本大学: 関与なし(対象大学)

  58. 2026 · ICML

    Reliable Thinking with Images

    Haobin Li, Yutong Yang, Yijie Lin, daixiang, Mouxing Yang, Xi Peng

    Machine Learning

    日本大学: 関与なし(対象大学)

  59. 2026 · ICML

    Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics

    Xiaoyan Xing, Xiao Zhang, Sezer Karaoglu, Theo Gevers, Anand Bhattad

    Machine Learning

    日本大学: 関与なし(対象大学)

  60. 2026 · ICML

    Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacers

    Yuan Guo, Wanqi Zhang, Xu Wang

    Machine Learning

    日本大学: 関与なし(対象大学)

  61. 2026 · ICML

    Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining

    Thiziri Nait Saada, Louis Béthune, Michal Klein, David Grangier, Marco Cuturi, Pierre Ablin

    Machine Learning

    日本大学: 関与なし(対象大学)

  62. 2026 · ICML

    Removing Sandbagging in LLMs by Training with Weak Supervision

    Emil Ryd, Henning Bartsch, Julian Stastny, Joe Benton, Vivek Hebbar

    Machine Learning

    日本大学: 関与なし(対象大学)

  63. 2026 · ICML

    Reparameterization Flow Policy Optimization

    Hai Zhong, Zhuoran Li, Xun Wang, Longbo Huang

    Machine Learning

    日本大学: 関与なし(対象大学)

  64. 2026 · ICML

    Reparameterization Proximal Policy Optimization

    Hai Zhong, Xun Wang, Zhuoran Li, Longbo Huang

    Machine Learning

    日本大学: 関与なし(対象大学)

  65. 2026 · ICML

    RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress

    Ruixuan Huang, Qingyue Wang, Hantao Huang, Yudong Gao, Dong CHEN, Shuai Wang, Wei Wang

    Machine Learning

    日本大学: 関与なし(対象大学)

  66. 2026 · ICML

    Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following

    Kongcheng Zhang, QI YAO, Shunyu Liu, Wenjian Zhang, Min Cen, Yang Zhou, Wenkai Fang, Yiru Zhao, Baisheng Lai, Mingli Song

    Machine Learning

    日本大学: 関与なし(対象大学)

  67. 2026 · ICML

    Representation Drift Compensation: A Near-Zero Inference Cost Enhancement for LLM Decomposition

    Xinhao Huang, You-Liang Huang, Zeyi Wen

    Machine Learning

    日本大学: 関与なし(対象大学)

  68. 2026 · ICML

    Representation Learning for Equivariant Inference with Guarantees

    Daniel Felipe Ordonez Apraez, Vladimir Kostic, Alek Fröhlich, Vivien Brandt, Karim Lounici, Massimiliano Pontil

    Machine Learning

    日本大学: 関与なし(対象大学)

  69. 2026 · ICML

    Representation Unlearning: Forgetting through Information Compression

    Antonio Almudévar, Alfonso Ortega

    Machine Learning

    日本大学: 関与なし(対象大学)

  70. 2026 · ICML

    Representational Curvature Modulates Behavioral Uncertainty in Large Language Models

    Jack King, Evelina Fedorenko, Eghbal Hosseini

    Machine Learning

    日本大学: 関与なし(対象大学)

  71. 2026 · ICML

    Representational Similarity and Model Behavior in Multi-Agent Interaction

    Yujin Potter, Seun Eisape, Shiyang Lai, Alexander Huth, James Evans, Been Kim, Jacob Eisenstein, Dawn Song, Alane Suhr

    Machine Learning

    日本大学: 関与なし(対象大学)

  72. 2026 · ICML

    Required Spine Optional Limbs: Heterogeneous Federated Learning via Backbone-sharing and Activation-guided Selection

    Mingsheng Cao, Hongliang Chen, Ming Hu, Fei Gao, Qiaolong Ding, Wenke Huang, Xiaofei Xie, Junlong Zhou

    Machine Learning

    日本大学: 関与なし(対象大学)

  73. 2026 · ICML

    Reranker Helps, but Not Enough: Towards Strong Poisoning Attacks Against Retrieval-Augmented Generation

    Xiaokun Yang, Jian Liang, Yesheng Liu, Xin Xiong, Ran He, Tieniu Tan

    Machine Learning

    日本大学: 関与なし(対象大学)

  74. 2026 · ICML

    ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning

    Zihan Lin, Xiaohan Wang, Jie Cao, Jiajun Chai, Li Wang, Xiaodong Lu, Wei Lin, Ran He, Guojun Yin

    Machine Learning

    日本大学: 関与なし(対象大学)

  75. 2026 · ICML

    Residual Context Diffusion Language Models

    Yuezhou Hu, Harman Singh, Monishwaran Maheswaran, Haocheng Xi, Coleman Hooper, Jintao Zhang, Aditya Tomar, Michael Mahoney, Sewon Min, Mehrdad Farajtabar, Kurt Keutzer, Amir Gholaminejad, Chenfeng Xu

    Machine Learning

    日本大学: 関与なし(対象大学)

  76. 2026 · ICML

    Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting

    Wentao Gao, Jiuyong Li, Lin Liu, Thuc Le, Jixue Liu, Yanchang Zhao, Yun Chen

    Machine Learning

    日本大学: 関与なし(対象大学)

  77. 2026 · ICML

    Resilient Coresets and Clustering

    Ashkan Norouzi-Fard, Silvio Lattanzi, MohammadHossein Bateni, Morteza Monemizadeh

    Machine Learning

    日本大学: 関与なし(対象大学)

  78. 2026 · ICML

    Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification

    Zanwu Liu, Chao Yuan, Bo Li, Xiaowei Zhang, Guanglin Niu

    Machine Learning

    日本大学: 関与なし(対象大学)

  79. 2026 · ICML

    Resolving Blind Inverse Problems under Dynamic Range Compression via Structured Forward Operator Modeling

    Muyu Liu, Xuanyu Tian, Chenhe Du, Qing Wu, Hongjiang Wei, Yuyao Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  80. 2026 · ICML

    Resolving the Timestep Scaling Paradox in Spiking Neural Networks with a Timestep-Scalable Neuron Model

    Binghao Ye, Wenjuan Li, Dengfeng Xue, Bing Li, Weiming Hu, Dong Liang, Kun Shang

    Machine Learning

    日本大学: 関与なし(対象大学)

  81. 2026 · ICML

    Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement

    Yunjian Zhang, Sudong Wang, Yang Li, Peiran Xu, Conghao Zhou, Xiaoyue Ma, Jianing Li, Yao Zhu

    Machine Learning

    日本大学: 関与なし(対象大学)

  82. 2026 · ICML

    Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models

    Yuanwei Hu, Bo Peng, Yadan Luo, zhen fang, Ling Chen, Jie Lu

    Machine Learning

    日本大学: 関与なし(対象大学)

  83. 2026 · ICML

    Responsible Text-to-Image Diffusion: Interpretable and Linearly Controllable Semantics for Fair and Safe Generation

    Sayedmoslem Shokrolahi, Jae-Mo Kang, Il-Min Kim

    Machine Learning

    日本大学: 関与なし(対象大学)

  84. 2026 · ICML

    Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity

    Haotian Xu, Jiannan Yang, Tian Gao, Lily Weng, Tengfei Ma

    Machine Learning

    日本大学: 関与なし(対象大学)

  85. 2026 · ICML

    Restoring Exploration after Post-Training: Latent Exploration Decoding for Large Reasoning Models

    Wenhui Tan, Fiorenzo Parascandolo, Enver Sangineto, Jianzhong Ju, Zhenbo Luo, Qian Cao, Rita Cucchiara, Ruihua Song, Jian Luan

    Machine Learning

    日本大学: 関与なし(対象大学)

  86. 2026 · ICML

    Restoring Initial Noise Sensitivity in Text-to-Image Distillation through Geometric Alignment

    huayang Huang, Ruoyu Wang, Jinhui Zhao, Wei Deng, Daiguo Zhou, Jian Luan, Yu Wu, Ye Zhu

    Machine Learning

    日本大学: 関与なし(対象大学)

  87. 2026 · ICML

    Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting

    Howard Chen, Noam Razin, Karthik Narasimhan, Danqi Chen

    Machine Learning

    日本大学: 関与なし(対象大学)

  88. 2026 · ICML

    Rethink the Role of Neural Decoders in Quantum Error Correction

    Ge Yan, SHANCHUAN LI, Yuxuan Du

    Machine Learning

    日本大学: 関与なし(対象大学)

  89. 2026 · ICML

    Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models

    Zhijun Tu, Jian Li, Yuanyuan Xi, Siqi Liu, Chuanjian Liu, Hanting Chen, Jie Hu, Yunhe Wang

    Machine Learning

    日本大学: 関与なし(対象大学)

  90. 2026 · ICML

    Rethinking 3D Shape Generation: Diffusion over Superquadrics

    Zhiyang Liu, Wanze Li, Yuwei Wu, Chengran Yuan, JIAWEI SUN, Rui Zheng, Marcelo Ang Jr

    Machine Learning

    日本大学: 関与なし(対象大学)

  91. 2026 · ICML

    Rethinking Attention in Spiking Transformers: Overcoming Density Bias with Set Similarity

    JinGyo Lim, Seunggyu Jeong, Seong-Eun Kim

    Machine Learning

    日本大学: 関与なし(対象大学)

  92. 2026 · ICML

    Rethinking Calibration for Early-Exit Neural Networks

    Piotr Kubaty, Filip Szatkowski, Grzegorz Choczyński, Eric Nalisnick, Bartosz Wójcik

    Machine Learning

    日本大学: 関与なし(対象大学)

  93. 2026 · ICML

    Rethinking Code Complexity Through the Lens of Large Language Models

    Chen Xie, Xiaodong Gu, Yuling Shi, Beijun Shen

    Machine Learning

    日本大学: 関与なし(対象大学)

  94. 2026 · ICML

    Rethinking Contrastive Learning for Graph Collaborative Filtering: Limitations and a Simple Remedy

    Geon Lee, Sunwoo Kim, Kyungho Kim, Kijung Shin

    Machine Learning

    日本大学: 関与なし(対象大学)

  95. 2026 · ICML

    Rethinking Convergence in MoE Training: The Role of Routing Sparsity

    Weihao Zhu, Long Shi, Kang Wei, Zhe Wang, Yipeng Zhou, Haixia Zhang

    Machine Learning

    日本大学: 関与なし(対象大学)

  96. 2026 · ICML

    Rethinking Depth Pruning for Vision Transformers: A Heterogeneity-Aware Perspective

    Zhenfeng Su, Kang Zhao, Han Bao, Tao Yuan, Zhongzhe Hu, Xianzhi Yu, Wenxuan Wang

    Machine Learning

    日本大学: 関与なし(対象大学)

  97. 2026 · ICML

    Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle

    Xu Bai, Bin Lu, kunzhang, Shengbo Chen, Xinbing Wang, Chenghu Zhou, Meng Jin

    Machine Learning

    日本大学: 関与なし(対象大学)

  98. 2026 · ICML

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