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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
Discriminative Mixture-of-Experts on Graphs with Reliable Expert Fusion
Haoyue Deng, Menghui Wang, Yunlong Zhou, Jingyi Liu, Ran Zhang, Chunming Hu, Xiao Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Discriminative Visual Process Rewards for Scaling Thinking at Test-Time with Images
Bo-Wen Yin, Qize Yang, Boyuan Sun, Xihan Wei, Qibin Hou
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography
Bowen Shi, Weiwei Cao, Ruifeng Yuan, Wanxing Chang, Wenrui Dai, Hongkai Xiong, Ling Zhang, Jianpeng Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Disentangling Consensus and Value-Specific Representations for Controllable Pluralistic Value Alignment of LLMs
JianKui Zhou, Jing Yao, Xiaoyuan Yi, Peng Zhang, Ning Gu, Zhan Hu, Xing Xie, Tun Lu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Disentangling Geometry, Performance, and Training in Language Models
Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment
Jiajia Li, Xiaoyu Wen, Shuyue Hu, Qiaosheng Zhang, Zhen Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
Shengxian Ding, Haonan Gao, Pangpang Liu, Xinyuan Tian, Yize Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers
Shraman Pal, Can Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding
Liu Yu, Can Chen, PING KUANG, Zhikun Feng, Fan Zhou, Gillian Dobbie
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dismantling the Illusion of Vision-Language-Action Models Competence via Explicit Distributional Shifts
Xueyang Zhou, Yangming Xu, Guiyao Tie, Chaoran Hu, Bo Tao, xingwei zhao, Xiang Xiang, Pan Zhou, Lichao Sun, Yongchao Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models
Chen Liu, Xingzhi Sun, Xi Xiao, Alexandre Van Tassel, Ke Xu, Kristof Reimann, Danqi Liao, Mark Gerstein, Tianyang Wang, Xiao Wang, Smita Krishnaswamy
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection
Dahye Kim, Jaehyun Choi, Hyun Seok Seong, Seongho Kim, Donghun Lee, Sungwon Yi, Jang-Ho Choi
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dissecting Causal Mechanism Shifts via FANS: Function And Noise Separation
Gyeongdeok Seo, Jaeyoon Shim, Mingyu Kim, Hoyoon Byun, Yonghan Jung, Kyungwoo Song
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers
Yiran Huang, Karsten Roth, Quentin Bouniot, Wenjia Xu, Zeynep Akata
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dissecting Post-Training: Uncovering the Complementary Roles of SFT and RL for Document Parsing
Jun-Peng Jiang, An-Yang Ji, Shiyin Lu, Guodong Zheng, Weihong Zhang, Qing-Guo Chen, Weihua Luo, Kaifu Zhang, Long Chen, De-Chuan Zhan, Han-Jia Ye
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dissecting Quantization Error: A Concentration-Alignment Perspective
Marco Federici, Boris van Breugel, Paul Whatmough, Markus Nagel
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dissecting the Safety Circuit: Neuronal Intervention for Transferable Adversarial Attacks on VLMs
Chunlong Xie, Kangjie Chen, Shangwei Guo, Shudong Zhang, Jiamou Liu, Tianwei Zhang, Tao Xiang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
zhixin wang, Jiaming Xu, Tianyi Zhou, Mingjun Zhang, Liming Liu, JiaruiHu, Dian Yang, TongYu Wang, Ping Zhang, Jinlong Hou, Siyuan Feng, Yuan Qi, Yuan Cheng
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DistMatch: Adaptive Binning via Distribution Matching for Robust Sequential Conformal Prediction
Enver Menadjiev, Jihyeon Seong, Jisu Yeo, Jaesik Choi
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distillation Models are Good Samplers for Diffusion Reinforcement Learning
Zunxu Liu, Aiqiu Wu, Zhaofan Qiu, Yingwei Pan, Ting Yao, Tao Mei
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distilling Linearized Behavior into Non-linear Fine-Tuning for Effective Task Arithmetic
Thomas Sommariva, Francesca Morandi, Simone Calderara, Angelo Porrello
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs
Wentao Mo, Yang Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distilling Task-Level Coordination Policies for Generalizable Multi-Agent Cooperation
Zimo Zhai, Manjie Xu, Wei Liang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
Puning Yang, Junchi Yu, Qizhou Wang, Phil Torr, Bo Han, Xiuying Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distinguishing Imitation Error from Intrinsic Motion Learning Difficulty
Zhaorui Meng, Lu Yin, Xinrui Chen, Chengxu Zuo, Anjun Chen, Guo Shihui, Yipeng Qin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner
Kazusato Oko, Annie Ulichney, Nika Haghtalab, Han Bao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributed Direct Preference Optimization
Zhanhong Jiang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributed Stochastic $K$-Level Optimization Over Networks
Xinwen Zhang, Yihan Zhang, Hongchang Gao, Heng Huang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distribution Alignment for One-Shot Federated Learning via Optimal Transport
Daniele Berardini, Vito Paolo Pastore, Vittorio Murino
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distribution Matching Variational AutoEncoder
Sen Ye, Jianning Pei, Mengde Xu, Shuyang Gu, Chunyu Wang, Liwei Wang, Han Hu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
George Whittle, Juliusz Ziomek, Jacob Rawling, Michael A Osborne
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distribution-Calibrated Inference Time Compute for Thinking LLM-as-a-Judge
Hamid Dadkhahi, Firas Trabelsi, Parker Riley, Juraj Juraska, Mehdi Mirzazadeh
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributional Active Inference
Abdullah Akgül, Gulcin Baykal, Manuel Haussmann, Mustafa Mert Çelikok, Melih Kandemir
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributional Alignment Games for Answer-Level Fine-Tuning
Mehryar Mohri, Jon Schneider, Yifan Wu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributional Inverse Reinforcement Learning
Feiyang Wu, Ye Zhao, Anqi Wu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook
Jaehyeok Lee, Xiaoyuan Yi, Jing Yao, Hyunjin Hwang, Roy Lee, Xing Xie, JinYeong Bak
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributionally Robust Causal Abstractions
Yorgos Felekis, Theodoros Damoulas, Paris Giampouras
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributionally Robust Markov Games with Average Reward
Zachary Roch, Yue Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributionally Robust Reinforcement Learning from Human Feedback
Debmalya Mandal, Paulius Sasnauskas, Goran Radanovic
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
Yankai Chen, Hanrong Zhang, Bowei He, Philip Yu, Xue Liu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation
Boyuan Xiao, Bohong Chen, Yumeng Li, Ji Feng, Yao-Xiang Ding, Kun Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Divergence Decoding: Inference-Time Unlearning via Auxiliary Models
Humzah Merchant, Bradford Levy
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models
Yijie Tong, Yifan Hou, Shaobo Cui, Antoine Bosselut, Mrinmaya Sachan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Diversity Over Frequency: Rethinking Tool Use in Visual Chain-of-Thought Agents
Dong-Hee Kim, Reuben Tan, DONGHYUN KIM
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Diversity-Aware Recursive Feature Multiple Kernel Learning
nan cao, Xu Zhao, Teng Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning
Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
Tianhe Wu, Ruibin Li, Lei Zhang, Kede Ma
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Diversity-aware Weight Perturbation Promotes Robust Adaptation
Zibo Chen, Ruxin Li, Zilu Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection
Xiaolu Kang, Zhongyuan Wang, Jikang Cheng, Baojin Huang, Zhanhe Lei, Gang Wu, Qin Zou, Qian Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Divide and Contrast: Learning Robust Temporal Features without Augmentation
Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
Esha Singh, Dongxia Wu, Chien-Yi Yang, Tajana Rosing, Rose Yu, Yian Ma
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
Abhi Gupta, Polina Barabanshchikova, Vikas Garg, Samuel Kaski, Tommi Jaakkola
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Diving into Kronecker Adapters: Component Design Matters
Jiayu Bai, Danchen Yu, Zhenyu Liao, TianQi Hou, Feng Zhou, Robert Qiu, Zenan Ling
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Divisiveness-Consistent Label Distribution Learning
Yunan Lu, Haitao Wu, Weiwei Li, Lei Yang, Xiuyi Jia
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Activation Verbalization Methods Convey Privileged Information?
Millicent Li, Alberto Mario Ceballos Arroyo, Giordano Rogers, Naomi Saphra, Byron Wallace
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox
Jiacheng Pang, Ashutosh Chaubey, Mohammad Soleymani
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do LLMs Signal When They’re Right? Evidence from Neuron Agreement
Kang Chen, Yaoning Wang, Kai Xiong, Zhuoka Feng, Yu Minshen, Wenhe Sun, Haotian Chen, Yixin Cao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do LLMs “Feel”? Emotion Circuits Discovery and Control
Chenxi Wang, Yixuan Zhang, Ruiji Yu, Yufei Zheng, Lang Gao, Zirui Song, Zixiang Xu, Gus xia, Huishuai Zhang, Dongyan Zhao, Xiuying Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Language Models Track Entities Across State Changes?
Zilu Tang, Qiao Zhao, Gabriel Franco, Derry Wijaya, Aaron Mueller, Sebastian Schuster, Najoung Kim
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Neural Operators Forget Geometry? The Forgetting Hypothesis in Deep Operator Learning
Yanming Xia, Angelica Aviles-Rivero
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Sparse Autoencoders Identify Reasoning Features in Language Models?
George Ma, Zhongyuan Liang, Irene Y. Chen, Somayeh Sojoudi
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Text Edits Generalize to Visual Generation? Benchmarking Cross-Modal Knowledge Editing in UMMs
Xin Gao, Cheng Yang, Chufan Shi, Taylor Berg-Kirkpatrick
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Transformers Need Three Projections? Systematic Study of QKV Variants
Ali Kayyam, Anusha Gopal, M Lewis
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models
Zhongyu Yang, Dannong Xu, Yonghan Zhang, Kefan Chen, Xinyi Wang, Yang Xu, Wei Pang, Yingfang Yuan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Sagnik Mukherjee, Lifan Yuan, Pavan Jayasinha, Dilek Hakkani-Tür, Hao Peng
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Do-Prompt: Causal Interventions Meet Variational Prompt Bottlenecks
Xueting Chen, Jun-Jie Huang, Yan Yan, Long Lan, Yuhua Tang, Wenjing Yang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Doc-to-LoRA: Learning to Instantly Internalize Contexts
Rujikorn Charakorn, Edoardo Cetin, Shinnosuke Uesaka, Robert Lange
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DocHop: Benchmarking Out-of-domain Multi-hop Reasoning in Information-Dense Documents
Zhuoran Yu, Le T Nguyen, Jaden Park, Xinyi Gu, Zexue He, Soochahn Lee, Rogerio Feris, Yong Jae Lee
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DocOS: Towards Proactive Document-Guided Actions in GUI Agents
Jingjing Liu, Ziye Huang, Zihao Cheng, Zeming Liu, Jiahong Wu, Yuhang Guo, Kehai Chen, Yunhong Wang, Haifeng Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA
Pinaki Prasad Guha Neogi, Ahmad Mohammadshirazi, Ser-Nam Lim, Rajiv Ramnath
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Does AI Reviewer See the Full Picture? Attacking and Defending Multimodal Peer Review
Xinyu Zhao, Rana Khan, Zhen Xu, Zhen Tan, Tianlong Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
Maggie Ziyu Huan, Yuetai Li, Tuney Zheng, Xiaoyu Xu, Seungone Kim, Minxin Du, Radha Poovendran, Graham Neubig, Xiang Yue
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Does Reasoning Improve Seeing? Understanding When Vision-Language Models Benefit from Thinking
Jing Bi, Luchuan Song, Dingxin Zhang, Pinxin Liu, Guangyu Sun, Lianggong Wen, Weidong Cai, Chen Chen, Chenliang Xu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Does Reinforcement Fine-Tuning Improve Generalization of LLM Agents? An Empirical Study
Zhiheng Xi, Xin Guo, Jiaqi Liu, Jiazheng Zhang, Yutao Fan, Zhihao Zhang, Shichun Liu, Mingxu Chai, Xiaowei Shi, Yitao Zhai, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Does Your Reasoning Model Implicitly Know When to Stop Thinking?
Zixuan Huang, Xin Xia, Yuxi Ren, Jianbin Zheng, Xuanda Wang, Zhixia Zhang, Hongyan Xie, Songshi Liang, Zehao Chen, Xuefeng Xiao, Fuzhen Zhuang, Jianxin Li, deqing wang, Yikun Ban
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Does a Hybrid Space-Aware Randomized Defense Improve Empirical and Certified Adversarial Robustness?
Joy Dhar, Manish Pandey, Behzad Bozorgtabar, Nayyar Zaidi, Wenyu Zhang, Weihong Li, Tingting Mu, Dwarikanath Mahapatra, Mahsa Baktashmotlagh, Trung Le, Chen Chen, Sajib Mistry, Camila Gonzalez, Samira Ebrahimi Kahou, Lina Yao, Piotr Koniusz, Robert Fisher, Dinh Phung, Bohyung Han, Nuno Vasconcelos, Pietro Lió
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DomED: Redesigning Ensemble Distillation for Domain Generalization
Ziang Song, Zhou Zhidan, Zijun Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Domain Adaptation with Adaptive $f$-Divergence: Tighter Variational Representation and Generalization Bounds
Zhe Cheng, Fode Zhang, Yifan Zhu, Lingrui Wang, Jiaolong Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Domain Adaptive Object Detection via Dynamic Causal Refinement
Zeyu Ma, Jiaqi Huang, Yitong Qin, Ziqiang Zheng, Jiwei Wei, Jie Zou, Yang Yang, Heng Tao Shen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Domain Restriction via SAE Multi-Layer Transitions
Elias Shaheen, Avi Mendelson
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Domain Transfer Becomes Identifiable via a Single Alignment
Sagar Shrestha, Subash Timilsina, Hoang-Son Nguyen, Xiao Fu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Domain-Shift-Aware Conformal Prediction for Large Language Models
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
Mostafa Elhoushi, Alexander Pretko, Nolan Dey, Bin Zhang, Gavia Gray, Gurpreet Gosal, Abdulrahman Mahmoud, Shane Bergsma, Joel Hestness
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Don't Force the Fit: Bounded Log-Likelihood Loss for Enhanced Reasoning in Large Language Models
Feng Zhao, Hong Zhang, Yu Yang, Ruilin Zhao, Guandong Xu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Don't Forget Why You Started: Tackling Dual Forgetting in Vision-Language Continual Learning
Borui Kang, Jinrui Gu, Tao Feng, Qi Fan, Yinghuan Shi, Lei Wang, Wenbin Li, Yang Gao
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Don't Ignore the Tail: Decoupled Distillation Produces Top Maths Students on an Academic Budget
Sayantan Dasgupta, Trevor Cohn, Tim Baldwin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Don't Overthink with Pixels: Efficient Reasoning for Segmentation
Song Wang, Gongfan Fang, Lingdong Kong, Xiangtai Li, Jianyun Xu, Sheng Yang, Qiang Li, Jianke Zhu, Xinchao Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Don't Reinvent the Wheel, Just Realign the Spokes: Resource-Efficient Federated Fine-Tuning via Rank-Wise Expert Assembly
Yebo Wu, Jingguang Li, Zhijiang Guo, Li Li
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Don't Walk the Line: Boundary Guidance for Filtered Generation
Sarah Ball, Andreas Haupt
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Doppler Prompting for Stable mmWave-based Human Pose Estimation
Shuntian Zheng, Jiaqi Li, Xiaoman Lu, Shuai He, Yu Guan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Doubly Outlier-Robust Online Infinite Hidden Markov Model
Horace Yiu, Leandro Sánchez-Betancourt, Alvaro Cartea, Gerardo Duran-Martin
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Doubly Regularized Markov Decision Processes for Robust Reinforcement Learning
Yiting He, Zhishuai Liu, Pan Xu
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Doubly Robust Distributionally Robust Offline Contextual Pricing
Min Xu, Xinyi Yin, Yunfan Zhang, Yuxuan Han, Houcai Shen, Caihua Chen
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations
Wei Liu, Jiawei Xu, Yingru Li, Longtao Zheng, Tianjian Li, Qian Liu, Junxian He
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Draft-and-Audit Reinforcement Learning for Optimization Modeling
Zeping Min, Weihang Xu, Zhengzhong You, Wotao Yin, Xinshang Wang
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination
Jonathan Spieler, Sven Behnke
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DreamDojo: A Real-Time Robot World Model from Large-Scale Human Videos
Shenyuan Gao, William Liang, Kaiyuan Zheng, Ayaan Malik, Seonghyeon Ye, Sihyun Yu, Wei-Cheng Tseng, Yuzhu Dong, Kaichun Mo, Chen-Hsuan Lin, Jiannan Xiang, Yuqi Xie, Ruijie Zheng, Dantong Niu, Pooya Jannaty, Jinwei Gu, Jun Zhang, Jitendra Malik, Pieter Abbeel, Ming-Yu Liu, Yuke Zhu, Joel Jang, Jim Fan
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation
Xu Guo, Fulong Ye, Qichao Sun, Liyang Chen, Bingchuan Li, Pengze Zhang, Jiawei Liu, Songtao Zhao, Qian HE, Xiangwang Hou
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
Dreaming in Code for Curriculum Learning in Open-Ended Worlds
Konstantinos Mitsides, Maxence Faldor, Antoine Cully
Machine Learning
日本大学: 関与なし（対象大学）
2026 · ICML
DriveWorld-VLA: Unified Latent-Space World Modeling with Vision–Language–Action for Autonomous Driving
Feiyang Jia, Lin Liu, Ziying Song, Caiyan Jia, Hangjun Ye, Xiaoshuai Hao, Long Chen
Machine Learning
日本大学: 関与なし（対象大学）