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
VideoSEG-O3: A Multi-turn Reinforcement Learning Framework for Reasoning Video Object Segmentation
Ming Dai, Sen Yang, Boqiang Duan, Boyuan Tong, Jiedong Zhuang, Wankou Yang, Jingdong Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos
Wenqi Liu, Yunxiao Wang, Shijie Ma, Meng Liu, Qile Su, Tianke Zhang, Haonan Fan, Changyi Liu, Kaiyu Jiang, Jiankang Chen, Kaiyu Tang, Bin Wen, Fan Yang, Tingting Gao, Han Li, Yinwei Wei, Xuemeng Song
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VideoTrace-R1: Long Video-based Retrieval-Augmented Generation via Reinforcement Learning
Zongsheng Cao, Anran Liu, Jun Xie, Feng Chen, Lang Chen, Jing Li, zhepeng Wang, Zigan Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning
Hao Tan, jun lan, Senyuan Shi, Zichang Tan, Zijian Yu, Huijia Zhu, Weiqiang Wang, Jun Wan, Zhen Lei
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
View Space: Learning Representation across Arbitrary Graphs
Dooho Lee, Myeong Kong, Minho Jeong, Jaemin Yoo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models
Ruishu Zhu, Zhihao Huang, Jiacheng Sun, Ping Luo, Hongyuan Zhang, Xuelong Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Vision Language Models Cannot Reason About Physical Transformation
Dezhi Luo, Yijiang Li, Maijunxian Wang, Tianwei Zhao, Bingyang Wang, siheng wang, Pinyuan Feng, Pooyan Rahmanzadehgervi, Ziqiao Ma, Hokin Deng
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Vision Transformer Finetuning Benefits from Non-Smooth Components
Ambroise Odonnat, Chapel Laetitia, Romain Tavenard, Ievgen Redko
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models
Wenxuan Huang, Yu Zeng, Qiuchen Wang, Zhen Fang, Shaosheng Cao, Zheng Chu, Qingyu Yin, Shuang Chen, Zhenfei Yin, Lin Chen, Zehui Chen, Yao Hu, Phil Torr, Feng Zhao, Wanli Ouyang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Vision-Language-Action Pretraining from Large-Scale Human Videos
Hao Luo, Yicheng Feng, Wanpeng Zhang, Sipeng Zheng, Ye Wang, Haoqi Yuan, jiazheng liu, Chaoyi Xu, Haiweng Xu, Qin Jin, Zongqing Lu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Vision-aligned Latent Reasoning for Multi-modal Large Language Model
Byungwoo Jeon, Yoonwoo Jeong, Hyunseok Lee, Minsu Cho, Jinwoo Shin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning
Hengbo Xu, Shengjie Jin, Yanbiao Ma, Zhiwu Lu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VisionWebDev: A Hierarchical Benchmark for Visual Website Development with Agent Verification
Zehai He, Wenyi Hong, ZHEN YANG, Ziyang Pan, Mingdao Liu, Xiaotao Gu, Jie Tang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Visual Implicit Autoregressive Modeling
Pengfei Jiang, Jixiang Luo, Luxi Lin, Zhaohong Huang, Xuelong Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Visual Para-Thinker: Divide-and-Conquer Reasoning for Visual Comprehension
Haoran Xu, hongyu wang, Jiaze Li, Shunpeng Chen, Zizhao Tong, Jianzhong Ju, Zhenbo Luo, Jian Luan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Visual Persuasion: What Influences Decisions of Vision-Language Models?
Manuel Cherep, Pranav M R, Pattie Maes, Nikhil Singh
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge
Yueqi Song, Tianyue Ou, Yibo Kong, Zecheng Li, Graham Neubig, Xiang Yue
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VisualScore: Learning Holistic Visual Quality Scores via Multi-Task Reasoning
Yiting Lu, Fengbin Guan, Yixin Gao, Yan Zhong, Xinge Peng, Jiakang Yuan, Yihao Liu, Bo Zhang, Xin Li, Zhibo Chen, Weisi Lin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos
Qiucheng Wu, Handong Zhao, Zhixin Shu, Jing Shi, Yang Zhang, Shiyu Chang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VlogReward: Learning Multi-Dimensional Evaluation for Vlog Editing
Yexiang Liu, Wen Zhong, Sijie Zhu, Xin Gu, Fan Chen, Junxian Duan, Jie Cao, Longyin Wen, Zhenfang Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
VocSim A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio
Maris Basha, Anja T. Zai, Sabine Stoll, Richard Hahnloser
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction
Jiazhen Huang, Zhiming Liu, Changhu Wang, Wei Ju, Ziyue Qiao, Xiao Luo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
Simin Li, Zihao Mao, Zheng Yuwei, Linhao Wang, Ruixiao Xu, Chengdong Ma, Zhiqian Liu, Xin Yu, Yuqing Ma, Xin Wang, Jie Luo, Bo An, Yaodong Yang, Weifeng Lv, Xianglong Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WAVE: Window-Aware Vocabulary-Efficient Early-Exit for Training-Free LLM Acceleration
Seonggeun Kim, Gilha lee, Hyun Kim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WBMM: Windowed Batch Matrix Multiplication for Efficient Large Receptive Field Convolution
Wan Song, Zhou Wei, Rui Wang, Jun Yu, Toru Kurihara, Xu Jiajia, shu zhan
Machine Learning
日本大学: 高知工科大学
2026 · ICML
WET: Mitigating World-Conditioned Knowledge Conflicts via World Entropy Tethering
Zixuan Wang, Yifei He, Zihan Wang, Kun Wang, Chaomeng chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WEVSR: Video Diffusion Generators for Real-World Video Super‑Resolution with Wavelet-Enhanced VAE Encoder
Yuying Chen, Liu, Linyan Jiang, Qifan Gao, Xianguo Zhang, Jianhou Gan, Wenqi Ren
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WF-Bench: A Benchmark for Neural-Network WaveFunction Expressivity and Scaling Laws
Lixing Zhang, Guijing Duan, Di Luo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WFR-MFM: One-Step Inference for Dynamic Unbalanced Optimal Transport
Xinyu Wang, Ruoyu Wang, Qiangwei Peng, Peijie Zhou, Tiejun Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling
Michael Aich, Andreas Fürst, Florian Sestak, Carlos Ruiz-Gonzalez, Niklas Boers, Johannes Brandstetter
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WISE: World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
Yuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin, Bin Lin, Peng Jin, Jiaqi Liao, Chaoran Feng, Fanqing Meng, Kun-Peng Ning, Bin Zhu, Li Yuan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WMVLM: Evaluating Diffusion Model Image Watermarking via Vision-Language Models
Zijin Yang, Yu Sun, Kejiang Chen, jiawei zhao, Jun Jiang, Weiming Zhang, Nenghai Yu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning
Gagan Mundada, Zihan Huang, Rohan Surana, Sheldon Yu, Jennifer Y Zhang, Xintong Li, Tong Yu, Lina Yao, Jingbo Shang, Julian McAuley, Junda Wu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
Jiale Chen, Vage Egiazarian, Roberto Castro, Torsten Hoefler, Dan Alistarh
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Wait, Wait, Wait... Why Do Reasoning Models Loop?
Charilaos Pipis, Shivam Garg, Vasilis Kontonis, Vaishnavi Shrivastava, Akshay Krishnamurthy, Dimitris Papailiopoulos
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Walrus: A Cross-domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah, Bruno Régaldo-Saint Blancard, François Rozet, Cristiana Diaconu, Lucas Meyer, Kaze Wong, Hadi Sotoudeh, Alberto Bietti, Irina Espejo, Rio Fear, Siavash Golkar, Tom Hehir, Keiya Hirashima, Geraud Krawezik, Francois Lanusse, Rudy Morel, Ruben Ohana, Liam Parker, Mariel Pettee, Jeff Shen, Kyunghyun Cho, Miles Cranmer, Shirley Ho
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving
Chiheng Lou, Sheng Qi, Rui Kang, Yong Zhang, Chen Sun, pengcheng wang, Xuanzhe Liu, Xin Jin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Wasserstein Geometry-Aware Adaptive Control via Meta-Learning
Xingyu Yang, Hanzhang Qu, Ye Cao, Jianfu Cao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding
AN HUANG, Junggab Son, Zuobin Xiong
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WatchLog: Efficient and Interpretable Event Reasoning for Endpoint Detection and Response Logs with Multimodal LLMs
Hongyi Zhou, Jianfeng Pan, Min Peng, Shaomang Huang, Xuling Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WaterSIC: information-theoretically (near) optimal linear layer quantization
Egor Lifar, Semyon Savkin, Or Ordentlich, Yury Polyanskiy
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Watermarking Graph Neural Networks via Explanations for Ownership Protection
Jane Downer, Yingdan Shi, Ziyan Liu, Ren Wang, Binghui Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Watermarking LLM Agent Trajectories
Wenlong Meng, Chen GONG, Terry Yue Zhuo, Fan Zhang, Kecen Li, Zheng Liu, Zhou Yang, Chengkun Wei, Wenzhi CHEN
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WaveSSM: Multiscale State-Space Models for Non-stationary Signal Attention
Ruben Solozabal, Velibor Bojkovic, Hilal AlQuabeh, Klea Ziu, Kentaro Inui, Martin Takac
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
Aiwei Liu, Minghua He, Shaoxun Zeng, Sijun Zhang, Linhao Zhang, Chuhan Wu, Wei Jia, Yuan Liu, Zhou Xiao, Jie Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
Jing Jia, Wei Yuan, Sifan Liu, Liyue Shen, Guanyang Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weak-to-Strong Generalization via Bregman Bias–Variance Decomposition
Gengze Xu, Wei Yao, Ziqiao Wang, Yong Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weakly Supervised Cross-Modal Learning for 4D Radar Scene Flow Estimation
Jingyun Fu, Zhiyu Xiang, Na Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection
Fatemeh Pesaran zadeh, Seyeon Choi, Xing Han Lù, Siva Reddy, Gunhee Kim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation
Zinan Zheng, Yang Liu, Nuo Chen, Juepeng Zheng, Hong Cheng, Jia Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weaving Graph over Tokens: Contextualizing Structured Sequences for LLMs
Jiaxuan Chen, Zixing Zhang, Ruijun Mao, Wei Sun, Zhicheng Liang, Yuhang Zhang, Yaxi Liu, Fangxin Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weaving in the Clouds: Achieving Synergistic Collaboration among LLM Agents via Federated Learning
Jiaxing Zhao, Hongbin Xie, Yuzhen Lei, Xuan Song, Zhuoran Shi, Lianxin Li, Shuangxue Liu, Haoran Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WebWorld: A Large-Scale World Model for Web Agent Training
Zikai Xiao, Jianhong Tu, Chuhang Zou, Yuxin Zuo, Zhi Li, Peng Wang, Bowen Yu, Fei Huang, Junyang Lin, Zuozhu Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weight Decay Improves Language Model Plasticity
Tessa Han, Sebastian Bordt, Hanlin Zhang, Sham Kakade
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weight Updates as Activation Shifts: A Principled Framework for Steering
Dyah Adila, John Cooper, Alexander Yun, Avi Trost, Frederic Sala
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weight-Space Learning for Certifiable Few-shot Transfer Learning
Fady Rezk, Royson Lee, Henry Gouk, Timothy Hospedales, Minyoung Kim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weight-sparse transformers have interpretable circuits
Leo Gao, Achyuta Rajaram, Jacob Coxon, Soham Govande, Bowen Baker, Daniel Mossing
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WeightCLIP: Aligning Datasets and Models for Weight Space Learning
Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk, Léo Meynent, Damian Borth
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer
Yifan Zhang, Wei Bi, Kechi Zhang, Dongming Jin, Jie Fu, Zhi Jin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Welfare-Optimal Classification with Accuracy Auctions
Bana Sadi, Eden Saig, Nir Rosenfeld
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Well-Posed KL-Regularized Control via Wasserstein and Kalman–Wasserstein KL Divergences
Viktor Stein, Adwait Datar, Nihat Ay
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
Yuchen Wang, Jiangtao Kong, Sizhe Wei, Xiaochang Li, Haohong Lin, Hongjue Zhao, Tianyi Zhou, Lu Gan, Huajie Shao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Yunzhen Feng, Julia Kempe, Cheng Zhang, Parag Jain, Anthony Hartshorn
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Do Agents Learn from Trajectory-SFT: Semantics or Interfaces?
Weizheng Gu, Chengze Li, Zhuohao Yu, Mengyuan Sun, Zhibang Yang, Wei Wang, Hongrui Jia, Shikun Zhang, Wei Ye
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Does Flow-Matching Bring to TD-Learning?
Bhavya Agrawalla, Michal Nauman, Aviral Kumar
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Does Preference Learning Recover from Pairwise Comparison Data?
Rattana Pukdee, Nina Balcan, Pradeep Ravikumar
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Does Thompson Sampling Optimize?
Yanlin Qu, Hongseok Namkoong, Assaf Zeevi
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Does Vision Tool-Use Reinforcement Learning Really Learn? Disentangling Tool-Induced and Intrinsic Effects for Crop-and-Zoom
Ma Yan, Weiyu Zhang, Tianle Li, Du Linge, Xuyang Shen, Pengfei Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What If We Allocate Test-Time Compute Adaptively?
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Ali Subhan, Hassan Rizwan, Ayesha Mohsin, Dean Hougen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
Fan Zhang, Shiming Fan, Hua Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
Danny Wang, Ruihong Qiu, Zi Huang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What LLMs Explain Is Not What They Believe: Evaluating Explanation Sufficiency Under Models' Own Input Beliefs
Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Language is This? Ask Your Tokenizer.
Clara Meister, Ahmetcan Yavuz, Pietro Lesci, Tiago Pimentel
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Linear Probes Miss: Multi-View Probing for Weight-Space Learning
Eunwoo Heo, Kyeongkook Seo, Jaejun Yoo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis
Xinghao Chen, Chak Tou Leong, Wenjin Guo, Jian Wang, Wenjie Li, Xiaoyu Shen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Makes Synthetic Data Effective in Image Segmentation
Jinjin Zhang, Xiefan Guo, Yizhou jin, Nan Zhou, Di Huang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Makes Value Learning Efficient in Residual Reinforcement Learning?
Guozheng Ma, Lu Li, Haoyu Wang, Zixuan Liu, Pierre-Luc Bacon, Dacheng Tao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Makes a Desired Graph for Relational Deep Learning?
Yao Cheng, Siqiang Luo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Makes a Representation Good for Single-Cell Perturbation Prediction?
Wenkang Jiang, Yuhang Liu, Yichao Cai, Erdun Gao, Jiayi Dong, Ehsan Abbasnejad, Lina Yao, Javen Qinfeng Shi
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Makes a Strong Model? A Unified Spectral Analysis of Knowledge Transfer over High-dimensional Linear Regression
Wendao Wu, Fangqing Zhang, Haihan Zhang, Cong Fang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Preferences Can—and Cannot—Predict in Multi-Agent Online Learning
Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code
Yuze Zhao, Junpeng Fang, Lu Yu, Zhenya Huang, Kai Zhang, Qing Cui, Qi Liu, JUN ZHOU, Enhong Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What Reward Structure Enables Efficient Sparse-Reward RL? A Proof-of-Concept with Policy-Aware Matrix Completion
Ibne Farabi Shihab, SANJEDA AKTER, Anuj Sharma
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity
Haoxi Li, Qinglin Hou, Jianfei Ma, Jinxiang Lai, Tao Han, Sikai Bai, Jingcai Guo, Jie ZHANG, Song Guo
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
Shuqi Gu, Yongxiang Zhao, Baoyu Jing, Kan Ren
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
What is Missing? Explaining Neurons Activated by Absent Concepts
Robin Hesse, Simone Schaub-Meyer, Janina Hesse, Bernt Schiele, Stefan Roth
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets
Chris Chiu, Simpson Zhang, Mihaela van der Schaar
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
Mubashara Akhtar, Anka Reuel, Prajna Soni, Sanchit Ahuja, Pawan Sasanka Ammanamanchi, Ruchit Rawal, Vilém Zouhar, Srishti Yadav, Chenxi Whitehouse, Dayeon Ki, Jennifer Mickel, Leshem Choshen, Marek Šuppa, Jan Batzner, Jenny Chim, Jeba Sania, Yanan Long, Hossein A. Rahmani, Christina Knight, Yiyang Nan, Jyoutir Raj, Yu Fan, Shubham Singh, Subramanyam Sahoo, Eliya Habba, Usman Gohar, Siddhesh Pawar, Robert Scholz, Arjun Subramonian, Jingwei Ni, Mykel Kochenderfer, Sanmi Koyejo, Mrinmaya Sachan, Stella Biderman, Zeerak Talat, Avijit Ghosh, Irene Solaiman
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents
Yuting Ning, Jaylen Jones, Zhehao Zhang, Chentao Ye, Weitong Ruan, Junyi Li, Rahul Gupta, Huan Sun
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems
Haowen Xu, Xue Tan, Lei Ma, Zhihao Zhang, CHAO WANG, Qingze Wang, Ping Chen, Jun Dai, Xiaoyan Sun
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Attributes Disagree: Gradient Conflict in Image Aesthetic Assessment
Ye Wang, Maocai Dai, Jiang Xie, Xiuli Bi, Fei Tao, Xiao Li, Hong Yu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents
Jaylen Jones, Zhehao Zhang, Yuting Ning, Eric Fosler-Lussier, Pierre-Luc St-Charles, Yoshua Bengio, Dawn Song, Yu Su, Huan Sun
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Can We Trust Survival Model Evaluation ?
Ghanem BAHRINI, Sebastien Razakarivony, Jean-François Dupuy, Valerie Gares, Morgane Barbet-Massin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPs
Jose Aguilar Escamilla, Haoyang Hong, Jiawei Li, Haoyu Zhao, Xuezhou Zhang, Sanghyun Hong, Huazheng Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
Boqian Wu, Qiao Xiao, Patrik Okanovic, Tomasz Sternal, Maurice Keulen, Mykola Pechenizkiy, Elena Mocanu, Torsten Hoefler, Decebal Constantin Mocanu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Diffusion Language Models Hesitate: Detecting and Correcting Visual Hallucinations via Confidence Fluctuation
Wenzheng Song, Pei Chen, Yichen Tan, Zejian Li, Lingyun Sun
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models
Tong Xie, Ching-Yuan Bai, Yuanhao Ban, Yunqi Hong, Haoyu Li, Cho-Jui Hsieh
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Do Diffusion Models Learn to Generate Multiple Objects?
Yujin Jeong, Arnas Uselis, Iro Laina, Seong Joon Oh, Anna Rohrbach
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Do Graph Foundation Models Transfer? A Data-Centric Theory
Jiajun Zhu, Ying Chen, Peihao Wang, Yixuan He, Pan Li, Aditya Akella, Zhangyang “Atlas” Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression
Xinnan Dai, Kai Yang, cheng Luo, Shenglai Zeng, Kai Guo, Jiliang Tang
Machine Learning
日本大学: 関与なし（対象大学）