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Machine Learning

2025 · 3,330 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。

2025 · ICML
Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
Ruchika Chavhan, Abhinav Mehrotra, Malcolm Chadwick, Alberto Gil Couto Pimentel Ramos, Luca Morreale, Mehdi Noroozi, Sourav Bhattacharya
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Update Your Transformer to the Latest Release: Re-Basin of Task Vectors
Filippo Rinaldi, Giacomo Capitani, Lorenzo Bonicelli, Donato Crisostomi, Federico Bolelli, Elisa Ficarra, Emanuele Rodolà, Simone Calderara, Angelo Porrello
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
Sunny Sanyal, Hayden Prairie, Rudrajit Das, Ali Kavis, Sujay Sanghavi
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VCT: Training Consistency Models with Variational Noise Coupling
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VIP: Vision Instructed Pre-training for Robotic Manipulation
Zhuoling Li, Liangliang Ren, Jinrong Yang, Yong Zhao, Xiaoyang Wu, Zhenhua Xu, Xiang Bai, Hengshuang Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VTGaussian-SLAM: RGBD SLAM for Large Scale Scenes with Splatting View-Tied 3D Gaussians
Pengchong Hu, Zhizhong Han
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Validating Mechanistic Interpretations: An Axiomatic Approach
Nils Palumbo, Ravi Mangal, Zifan Wang, Saranya Vijayakumar, Corina S. Pasareanu, Somesh Jha
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Value-Based Deep RL Scales Predictably
Oleh Rybkin, Michal Nauman, Preston Fu, Charlie Victor Snell, Pieter Abbeel, Sergey Levine, Aviral Kumar
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Variance as a Catalyst: Efficient and Transferable Semantic Erasure Adversarial Attack for Customized Diffusion Models
Jiachen Yang, Yusong Wang, Yanmei Fang, Yunshu Dai, Fangjun Huang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Variance-Reduced Forward-Reflected-Backward Splitting Methods for Nonmonotone Generalized Equations
Quoc Tran-Dinh
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Variational Control for Guidance in Diffusion Models
Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, Stephan Mandt
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Variational Counterfactual Intervention Planning to Achieve Target Outcomes
Xin Wang, Shengfei Lyu, Chi Luo, Xiren Zhou, Huanhuan Chen
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Variational Learning of Fractional Posteriors
Kian Ming A. Chai, Edwin V. Bonilla
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Variational Phylogenetic Inference with Products over Bipartitions
Evan Sidrow, Alexandre Bouchard-Cote, Lloyd T Elliott
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Variational Rectified Flow Matching
Pengsheng Guo, Alex Schwing
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision
Marco Cipriano, Moritz Feuerpfeil, Gerard De Melo
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VerbalTS: Generating Time Series from Texts
Shuqi Gu, Chuyue Li, Baoyu Jing, Kan Ren
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible
Lin-Han Jia, Wen-Chao Hu, Jie-Jing Shao, Lan-Zhe Guo, Yu-Feng Li
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
Thomas Zeng, Shuibai Zhang, Shutong Wu, Christian Classen, Daewon Chae, Ethan Ewer, Minjae Lee, Heeju Kim, Wonjun Kang, Jackson Kunde, Ying Fan, Jungtaek Kim, Hyung Il Koo, Kannan Ramchandran, Dimitris Papailiopoulos, Kangwook Lee
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy
Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik, Konstantin Donhauser, Jason Hartford, Saber Saberian, Nil Sahin, Ihab Bendidi, Safiye Celik, Juan Sebastián Rodrı́guez Vera, Marta Fay, Imran S Haque, Oren Kraus
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, Jianyu Chen
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
Minting Pan, Yitao Zheng, Jiajian Li, Yunbo Wang, Xiaokang Yang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
Hila Chefer, Uriel Singer, Amit Zohar, Yuval Kirstain, Adam Polyak, Yaniv Taigman, Lior Wolf, Shelly Sheynin
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
Xilin Wei, Xiaoran Liu, Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Jian Tong, Haodong Duan, Qipeng Guo, Jiaqi Wang, Xipeng Qiu, Dahua Lin
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VinePPO: Refining Credit Assignment in RL Training of LLMs
Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, Nicolas Le Roux
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Vintix: Action Model via In-Context Reinforcement Learning
Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin, Ilya Zisman, Denis Tarasov, Alexander Nikulin, Vladislav Kurenkov
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Vision Graph Prompting via Semantic Low-Rank Decomposition
Zixiang Ai, Zichen Liu, Jiahuan Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Vision-Language Model Selection and Reuse for Downstream Adaptation
Hao-Zhe Tan, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Vision-Language Models Create Cross-Modal Task Representations
Grace Luo, Trevor Darrell, Amir Bar
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang, Jianling Sun, Chenghao Liu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Visual Abstraction: A Plug-and-Play Approach for Text-Visual Retrieval
Guofeng Ding, Yiding Lu, Peng Hu, Mouxing Yang, Yijie Lin, Xi Peng
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Mingi Jung, Saehyung Lee, Eunji Kim, Sungroh Yoon
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Visual Autoregressive Modeling for Image Super-Resolution
Yunpeng Qu, Kun Yuan, Jinhua Hao, Kai Zhao, Qizhi Xie, Ming Sun, Chao Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Visual Generation Without Guidance
Huayu Chen, Kai Jiang, Kaiwen Zheng, Jianfei Chen, Hang Su, Jun Zhu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models
Zahra Babaiee, Peyman Kiasari, Daniela Rus, Radu Grosu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Visual and Domain Knowledge for Professional-level Graph-of-Thought Medical Reasoning
Rina Bao, Shilong Dong, Zhenfang Chen, Sheng He, Ellen Grant, Yangming Ou
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Volume Optimality in Conformal Prediction with Structured Prediction Sets
Chao Gao, Liren Shan, Vaidehi Srinivas, Aravindan Vijayaraghavan
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Volume-Aware Distance for Robust Similarity Learning
Shuo Chen, Chen Gong, Jun Li, Jian Yang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Mengmeng Chen, Xiaohu Wu, Qiqi Liu, Tiantian He, Yew-Soon Ong, Yaochu Jin, Qicheng Lao, Han Yu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
Liang Chen, Xueting Han, Li Shen, Jing Bai, Kam-Fai Wong
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
Drew Prinster, Xing Han, Anqi Liu, Suchi Saria
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting
Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction
Fanmeng Wang, Minjie Cheng, Hongteng Xu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WILTing Trees: Interpreting the Distance Between MPNN Embeddings
Masahiro Negishi, Thomas Gärtner, Pascal Welke
Machine Learning
日本大学: 東京大学
2025 · ICML
WMAdapter: Adding WaterMark Control to Latent Diffusion Models
Hai Ci, Yiren Song, Pei Yang, Jinheng Xie, Mike Zheng Shou
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WMarkGPT: Watermarked Image Understanding via Multimodal Large Language Models
Songbai Tan, Xuerui Qiu, Yao Shu, Gang Xu, Linrui Xu, Xiangyu Xu, Huiping Zhuang, Ming Li, Fei Yu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
Yiheng Li, Cunxin Fan, Chongjian Ge, Seth Z. Zhao, Chenran Li, Chenfeng Xu, Huaxiu Yao, Masayoshi Tomizuka, Bolei Zhou, Chen Tang, Mingyu Ding, Wei Zhan
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Wait-Less Offline Tuning and Re-solving for Online Decision Making
Jingruo Sun, Wenzhi Gao, Ellen Vitercik, Yinyu Ye
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
Doron Haviv, Aram-Alexandre Pooladian, Dana Pe’Er, Brandon Amos
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Wasserstein Policy Optimization
David Pfau, Ian Davies, Diana L Borsa, João Guilherme Madeira Araújo, Brendan Daniel Tracey, Hado Van Hasselt
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
Tianjie Ju, Yi Hua, Hao Fei, Zhenyu Shao, Yubin Zheng, Haodong Zhao, Mong-Li Lee, Wynne Hsu, Zhuosheng Zhang, Gongshen Liu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WeGeFT: Weight-Generative Fine-Tuning for Multi-Faceted Efficient Adaptation of Large Models
Chinmay Savadikar, Xi Song, Tianfu Wu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Weak-to-Strong Generalization Even in Random Feature Networks, Provably
Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Weak-to-Strong Jailbreaking on Large Language Models
Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, William Yang Wang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Weakly Supervised Anomaly Detection via Dual-Tailed Kernel
Walid Durani, Tobias Nitzl, Claudia Plant, Christian Böhm
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
Zi-Hao Zhou, Jun-Jie Wang, Tong Wei, Min-Ling Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Weight matrices compression based on PDB model in deep neural networks
Xiaoling Wu, Junpeng Zhu, Zeng Li
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win
Lorenz Kummer, Samir Moustafa, Anatol Ehrlich, Franka Bause, Nikolaus Suess, Wilfried N. Gansterer, Nils Morten Kriege
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning?
Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire Tomlin, Sergey Levine, Aviral Kumar
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Keyon Vafa, Peter G. Chang, Ashesh Rambachan, Sendhil Mullainathan
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What If We Recaption Billions of Web Images with LLaMA-3?
Xianhang Li, Haoqin Tu, Mude Hui, Zeyu Wang, Bingchen Zhao, Junfei Xiao, Sucheng Ren, Jieru Mei, Qing Liu, Huangjie Zheng, Yuyin Zhou, Cihang Xie
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What Limits Bidirectional Model’s Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
Zuchao Li, Yonghua Hei, Qiwei Li, Lefei Zhang, Ping Wang, Hai Zhao, Baoyuan Qi, Liu Guoming
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities
Wendong Bu, Yang Wu, Qifan Yu, Minghe Gao, Bingchen Miao, Zhenkui Zhang, Kaihang Pan, Yunfei Li, Mengze Li, Wei Ji, Juncheng Li, Siliang Tang, Yueting Zhuang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What Makes In-context Learning Effective for Mathematical Reasoning
Jiayu Liu, Zhenya Huang, Chaokun Wang, Xunpeng Huang, Chengxiang Zhai, Enhong Chen
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What Makes a Good Feedforward Computational Graph?
Alex Vitvitskyi, João Guilherme Madeira Araújo, Marc Lackenby, Petar Veličković
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What can large language models do for sustainable food?
Anna Thomas, Adam Yee, Andrew Mayne, Maya B. Mathur, Dan Jurafsky, Kristina Gligorić
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
What makes an Ensemble (Un) Interpretable?
Shahaf Bassan, Guy Amir, Meirav Zehavi, Guy Katz
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Bad Data Leads to Good Models
Kenneth Li, Yida Chen, Fernanda Viégas, Martin Wattenberg
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Can Proxies Improve the Sample Complexity of Preference Learning?
Yuchen Zhu, Daniel Augusto De Souza, Zhengyan Shi, Mengyue Yang, Pasquale Minervini, Matt Kusner, Alexander D’Amour
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
Ziming Hong, Runnan Chen, Zengmao Wang, Bo Han, Bo Du, Tongliang Liu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
Chen Zeno, Hila Manor, Greg Ongie, Nir Weinberger, Tomer Michaeli, Daniel Soudry
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Do LLMs Help With Node Classification? A Comprehensive Analysis
Xixi Wu, Yifei Shen, Fangzhou Ge, Caihua Shan, Yizhu Jiao, Xiangguo Sun, Hong Cheng
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Dynamic Data Selection Meets Data Augmentation: Achieving Enhanced Training Acceleration
Suorong Yang, Peng Ye, Furao Shen, Dongzhan Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
Dong Xiao, Guangyao Chen, Peixi Peng, Yangru Huang, Yifan Zhao, Yongxing Dai, Yonghong Tian
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Maximum Entropy Misleads Policy Optimization
Ruipeng Zhang, Ya-Chien Chang, Sicun Gao
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class
Yujin Kim, Hyunsoo Kim, Hyunwoo J. Kim, Suhyun Kim
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
Min-Yeong Park, Won-Jeong Lee, Seong Tae Kim, Gyeong-Moon Park
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When and How Does CLIP Enable Domain and Compositional Generalization?
Elias Kempf, Simon Schrodi, Max Argus, Thomas Brox
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When can in-context learning generalize out of task distribution?
Chase Goddard, Lindsay M. Smith, Vudtiwat Ngampruetikorn, David J. Schwab
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When do neural networks learn world models?
Tianren Zhang, Guanyu Chen, Feng Chen
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When to Forget? Complexity Trade-offs in Machine Unlearning
Martin Van Waerebeke, Marco Lorenzi, Giovanni Neglia, Kevin Scaman
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When to retrain a machine learning model
Florence Regol, Leo Schwinn, Kyle Sprague, Mark Coates, Thomas Markovich
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
When, Where and Why to Average Weights?
Niccolò Ajroldi, Antonio Orvieto, Jonas Geiping
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Where is the Truth? The Risk of Getting Confounded in a Continual World
Florian Peter Busch, Roshni Ramanna Kamath, Rupert Mitchell, Wolfgang Stammer, Kristian Kersting, Martin Mundt
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
Shaokun Zhang, Ming Yin, Jieyu Zhang, Jiale Liu, Zhiguang Han, Jingyang Zhang, Beibin Li, Chi Wang, Huazheng Wang, Yiran Chen, Qingyun Wu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Which Attention Heads Matter for In-Context Learning?
Kayo Yin, Jacob Steinhardt
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Whitened CLIP as a Likelihood Surrogate of Images and Captions
Roy Betser, Meir Yossef Levi, Guy Gilboa
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Whoever Started the interference Should End It: Guiding Data-Free Model Merging via Task Vectors
Runxi Cheng, Feng Xiong, Yongxian Wei, Wanyun Zhu, Chun Yuan
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
Rylan Schaeffer, Hailey Schoelkopf, Brando Miranda, Gabriel Mukobi, Varun Madan, Adam Ibrahim, Herbie Bradley, Stella Biderman, Sanmi Koyejo
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas
Shiqi Chen, Tongyao Zhu, Ruochen Zhou, Jinghan Zhang, Siyang Gao, Juan Carlos Niebles, Mor Geva, Junxian He, Jiajun Wu, Manling Li
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
Like Jian, Dong Liu
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
Lukas Thede, Karsten Roth, Matthias Bethge, Zeynep Akata, Thomas Hartvigsen
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WildChat-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training
Benjamin Feuer, Chinmay Hegde
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale
Rogerio Bonatti, Dan Zhao, Francesco Bonacci, Dillon Dupont, Sara Abdali, Yinheng Li, Yadong Lu, Justin Wagle, Kazuhito Koishida, Arthur Bucker, Lawrence Keunho Jang, Zheng Hui
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
Adrien Cortes, Remi Rehm, Victor Letzelter
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Sunwoo Lee, Jaebak Hwang, Yonghyeon Jo, Seungyul Han
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
World Model Implanting for Test-time Adaptation of Embodied Agents
Minjong Yoo, Jinwoo Jang, Sihyung Yoon, Honguk Woo
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
WorldSimBench: Towards Video Generation Models as World Simulators
Yiran Qin, Zhelun Shi, Jiwen Yu, Xijun Wang, Enshen Zhou, Lijun Li, Zhenfei Yin, Xihui Liu, Lu Sheng, Jing Shao, Lei Bai, Ruimao Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
Thibault De Surrel, Fabien Lotte, Sylvain Chevallier, Florian Yger
Machine Learning
日本大学: 関与なし（対象大学）
2025 · ICML
Wyckoff Transformer: Generation of Symmetric Crystals
Nikita Kazeev, Wei Nong, Ignat Romanov, Ruiming Zhu, Andrey E Ustyuzhanin, Shuya Yamazaki, Kedar Hippalgaonkar
Machine Learning
日本大学: 関与なし（対象大学）