May 2, 2023 09:42

23 papers have been accepted at the International Conference on Machine Learning (ICML) 2023, a major conference on Artificial Intelligence (July 23-29, 2023 at the Hawaii Convention Center.
For more details, please refer to the link below.

[Website] https://icml.cc/Conferences/2023/

[Accepted Papers] https://icml.cc/Conferences/2023/AcceptedPapers

 

Oral Presentation

  • Memory-Based Dual Gaussian Processes for Sequential Learning
    Paul E Chang* (Aalto University)
    Prakhar Verma (Aalto University)
    ST John (Aalto University)
    Arno Solin (Aalto University)
    Mohammad Emtiyaz Khan (RIKEN AIP)
    *Remote collaborator at RIKEN-AIP
  • Diffusion Models Are Minimax Optimal Distribution Estimators
    Kazusato Oko (The University of Tokyo/RIKEN AIP)
    Shunta Akiyama (The University of Tokyo)
    Taiji Suzuki (The University of Tokyo/RIKEN AIP)

Poster Presentation

  • A Category-theoretical Meta-analysis of Definitions of Disentanglement
    Yivan Zhang (The University of Tokyo)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
    Chao Li (RIKEN AIP )
    Junhua Zeng (Guangdong University of Technology / RIKEN AIP)
    Chunmei Li (Harbin Engineering University / Waseda University)
    Cesar Caiafa (Instituto Argentino de Radioastronomía / RIKEN AIP)
    Qibin Zhao (RIKEN AIP)
  • Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input.
    Shokichi Takakura (The University of Tokyo/ RIKEN AIP)
    Taiji Suzuki (The University of Tokyo/ RIKEN AIP)
  • A Universal Unbiased Method for Classification from Aggregate Observations
    Zixi Wei (Chongqing University)
    Lei Feng (Nanyang Technological University)
    Bo Han (Hong Kong Baptist University)
    Tongliang Liu (The University of Sydney)
    Gang Niu (RIKEN AIP)
    Xiaofeng Zhu (University of Electronic Science and Technology of China)
    Heng Tao Shen (University of Electronic Science and Technology of China)
  • DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning.
    Tomoya Murata (NTT DATA Mathematical Systems Inc./ The University of Tokyo)
    Taiji Suzuki (The University of Tokyo/ RIKEN AIP).
  • Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
    Zhiqiang Yan (Nanjing University of Science and Technology)
    Xiang Li (Nankai University)
    Kun Wang (Nanjing University of Science and Technology)
    Shuo Chen (RIKEN AIP)
    Jun Li (Nanjing University of Science and Technology)
    Jian Yang (Nanjing University of Science and Technology)
  • Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
    Ruijiang Dong (Peking University)
    Feng Liu (University of Melbourne/RIKEN AIP)
    Haoang Chi (National University of Defense Technology)
    Tongliang Liu (The University of Sydney/ RIKEN AIP)
    Mingming Gong (University of Melbourne)
    Gang Niu (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
    Bo Han (Hong Kong Baptist University/RIKEN AIP)
  • FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
    Congyu Qiao (Southeast University)
    Ning Xu (Southeast University)
    Jiaqi Lv (RIKEN AIP)
    Yi Ren (Zhejiang Lab)
    Xin Geng(Southeast University)
  • GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
    Salah Ghamizi (University of Luxembourg/ RIKEN AIP)
    Jingfeng Zhang (RIKEN AIP)
    Maxime Cordy (University of Luxembourg)
    Mike Papadakis (University of Luxembourg)
    Masashi Sugiyama (RIKEN/The University of Tokyo)
    Yves Le Traon (University of Luxembourg)
  • Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
    Ryoma Sato (Kyoto University / RIKEN AIP)
  • How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
    Ming Li (Zhejiang Normal University)
    Sho Sonoda(RIKEN AIP)*corresponding author
    Feilong Cao (China Jiliang University)
    Yu Guang Wang (Shanghai Jiao Tong University)
    Jiye Liang (Shanxi University)
  • Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
    Hongxin Wei (Nanyang Technological University)
    Huiping Zhuang (South China University of Technology)
    Renchunzi Xie (Nanyang Technological University)
    Lei Feng (Nanyang Technological University)
    Gang Niu (RIKEN AIP)
    Bo An (Nanyang Technological University)
    Yixuan Li (University of Wisconsin, Madison)
  • Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
    Jongyeong Lee (The University of Tokyo/ RIKEN AIP)
    Junya Honda (Kyoto University/RIKEN AIP)
    Chao-Kai Chiang (The University of Tokyo)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
    Atsushi Nitanda (Kyushu Institute of Technology/ RIKEN AIP)
    Kazusato Oko (The University of Tokyo/ RIKEN AIP)
    Denny Wu (University of Toronto/ Vector Institute for Artificial Intelligence)
    Nobuhito Takenouchi (Kyushu Institute of Technology)
    Taiji Suzuki (The University of Tokyo/ RIKEN AIP)
  • Progressive Purification for Instance-Dependent Partial Label Learning
    Ning Xu (Southeast University)
    Biao Liu(Southeast University)
    Jiaqi Lv (RIKEN AIP)
    Congyu Qiao (Southeast University)
    Xin Geng (Southeast University)
  • Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
    Hayata Yamasaki (The University of Tokyo)*corresponding author
    Sathyawageeswar Subramanian (University of Warwick)
    Satoshi Hayakawa (University of Oxford)
    Sho Sonoda (RIKEN AIP)
  • Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
    Shion Takeno (Nagoya Institute of Technology/ RIKEN AIP)
    Yu Inatsu (Nagoya Institute of Technology)
    Masayuki Karasuyama (Nagoya Institute of Technology)
  • Revisiting Pseudo-Label for Single-Positive Multi-Label Learning
    Biao Liu (Southeast University)
    Ning Xu (Southeast University)
    Jiaqi Lv (RIKEN AIP)
    Xin Geng(Southeast University)
  • Simplifying Momentum-based Riemannian Submanifold Optimization,
    Wu Lin (The University of British Columbia)*
    Valentin Duruisseaux (University of California San Diego)
    Melvin Leok (University of California San Diego)
    Frank Nielsen (Sony Computer Science Laboratories, Inc.)
    Mohammad Emtiyaz Khan (RIKEN AIP)
    Mark Schmidt (The University of British Columbia)
    * Co-supervised PhD student by RIKEN’s PI
  • Tight and fast generalization error bound of graph embedding in metric space
    Atsushi Suzuki (King’s College London)
    Atsushi Nitanda (Kyushu Institute of Technology/ RIKEN AIP)
    Taiji Suzuki (The University of Tokyo/ RIKEN AIP)
    Jing Wang (University of Greenwich)
    Feng Tian (Duke Kunshan University)
    Kenji Yamanishi (The University of Tokyo)
  • Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
    Shion Takeno (Nagoya Institute of Technology, CyberAgent, RIKEN AIP)
    Masahiro Nomura (CyberAgent)
    Masayuki Karasuyama (Nagoya Institute of Technology)

 

 

 

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