May 21, 2021 15:49

Update: May 21, June 7

26 papers have been accepted at the International Conference on Machine Learning (ICML) 2021, a major conference on Artificial Intelligence (July 18-24, 2021, Online). For more details, please refer to the link below.

[Website] https://icml.cc/Conferences/2021

[Accepted Papers] https://icml.cc/Conferences/2021/AcceptedPapersInitial

Long talk (2 papers)

  • Confidence Scores Make Instance-dependent Label-noise Learning Possible
    Antonin Berthon (ENS & RIKEN AIP)+
    Bo Han (RIKEN AIP) Gang Niu (RIKEN AIP)
    Tongliang Liu (The University of Sydney)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
    Yivan Zhang (The University of Tokyo / RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)

Short talk (23 papers)

  • Active Learning for Distributionally Robust Level-Set Estimation.
    Yu Inatsu (Nagoya Institute of Technology)
    Shogo Iwazaki (Nagoya Institute of Technology)
    Ichiro Takeuchi (Nagoya Institute of Technology / RIKEN AIP)
  • Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
    Tomoya Murata (NTT DATA Mathematical Systems Inc. / The University of Tokyo)
    Taiji Suzuki (The University of Tokyo / RIKEN AIP)
  • Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
    Shida Lei (The University of Tokyo)
    Nan Lu (The University of Tokyo/RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Issei Sato (The university of Tokyo/RIKEN)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection Hanshu Yan (NUS)
    Jingfeng Zhang (RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Jiashi Feng (NUS)
    Vincent Tan (NUS)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Classification with Rejection Based on Cost-sensitive Classification
    Nontawat Charoenphakdee (The University of Tokyo / RIKEN AIP)
    Zhenghang Cui (The University of Tokyo / RIKEN AIP)
    Yivan Zhang (The University of Tokyo / RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
    Songhua Wu ( The University of Sydney )
    Xiaobo Xia ( The University of Sydney )
    Tongliang Liu ( The University of Sydney )
    Bo Han ( HKBU / RIKEN )
    Mingming Gong ( University of Melbourne )
    Nannan Wang ( Xidian University )
    Haifeng Liu ( Brain-Inspired Technology Co., Ltd. )
    Gang Niu ( RIKEN )
  • Large-Margin Contrastive Learning with Distance Polarization Regularizer
    Shuo Chen (RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Chen Gong (Nanjing University of Science and Technology )
    Jun Li (Nanjing University of Science and Technology )
    Jian Yang (Nanjing University of Science and Technology )
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo )
  • Learning Diverse-Structured Networks for Adversarial Robustness
    Xuefeng Du (Unversity of Wisconsin, Madison)
    Jingfeng Zhang (RIKEN AIP)
    Bo Han (RIKEN AIP)
    Tongliang Liu (The University of Sydney)
    Yu Rong (Tencent AI Lab)
    Gang Niu (RIKEN AIP)
    Junzhou Huang (University of Texas at Arlington)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Learning from Similarity-Confidence Data
    Yuzhou Cao (China Agricultural University)
    Lei Feng (Chongqing University)+
    Yitian Xu (China Agricultural University)
    Bo An (Nanyang Technological University)
    Gang Niu (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Lower-bounded proper losses for weakly supervised classification
    Shuhei Yoshida (NEC / RIKEN AIP)
    Takashi Takenouchi (Future University Hakodate/RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Maximum Mean Discrepancy is Aware of Adversarial Attacks
    Ruize Gao (Hong Kong Baptist University )
    Feng Liu (University of Technology Sydney)+
    Jingfeng Zhang (RIKEN AIP)
    Bo Han (RIKEN AIP)
    Tongliang Liu ( The University of Sydney )
    Gang Niu (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Mediated uncoupled learning: Learning functions without direct input-output correspondences
    Ikko Yamane (Université Paris Dauphine – PSL/RIKEN AIP)
    Junya Honda (Kyoto University / RIKEN AIP)
    Florian Yger (Université Paris-Dauphine )
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo )
  • More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method.
    Kazuya Sugiyama (Nagoya Institute of Technology)
    Vo Nguyen Le Duy (Nagoya Institute of Technology / RIKEN AIP)
    Ichiro Takeuchi (Nagoya Institute of Technology / RIKEN AIP)
  • Non-exponentially weighted aggregation: regret bounds for unbounded loss functions
    Pierre Alquier (RIKEN AIP)
  • On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
    Shunta Akiyama (The University of Tokyo)
    Taiji Suzuki (The University of Tokyo / RIKEN AIP)
  • Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
    Vu Nguyen (Amazon)*
    Tam Le (RIKEN AIP)*
    Makoto Yamada (Kyoto University/RIKEN AIP)
    Michael A Osborne (The University of Oxford
    (*: equal contribution)
  • Pointwise Binary Classification with Pairwise Confidence Comparisons
    Lei Feng (Chongqing University)+
    Senlin Shu (Southwest University)
    Nan Lu (The University of Tokyo/RIKEN)
    Bo Han (RIKEN AIP)
    Miao Xu (University of Queensland)
    Gang Niu (RIKEN AIP)
    Bo An (Nanyang Technological University)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
    Zeke Xie (The University of Tokyo/RIKEN AIP)
    Li Yuan (National Univerisity of Singapore)
    Zhanxing Zhu (Peking University)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo)
  • Post-selection inference with HSIC-Lasso
    Tobias Freidling (University of Cambridge)
    Benjamin Poignard (Osaka University/RIKEN AIP)
    Héctor Climente-González (RIKEN AIP)
    Makoto Yamada (Kyoto University/RIKEN AIP
  • Provably End-to-end Label-noise Learning without Anchor Points
    Xuefeng Li (University of New South Wales)
    Tongliang Liu (The University of Sydney)
    Bo Han (RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/The University of Tokyo )
  • Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
    Akira Nakagawa (Fujitsu Laboratories Ltd.)
    Keizo Kato (Fujitsu Laboratories Ltd.)
    Taiji Suzuki (The University of Tokyo / RIKEN AIP)
  • Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
    Alexander Immer (ETH and Max-Planck)+
    Matthias Bauer (Max-Planck and University of Cambridge)+
    Vincent Fortuin (ETH)
    Gunnar Rätsch (ETH and Max-Planck)
    Mohammad Emtiyaz Khan (RIKEN AIP)
  • Supervised Tree-Wasserstein Distance
    Yuki Takezawa (Kyoto University/RIKEN AIP)
    Ryoma Sato (Kyoto University/RIKEN AIP)
    Makoto Yamada (Kyoto University/RIKEN AIP)
  • Tractable structured natural gradient descent using local parameterizations
    Wu Lin (UBC)
    Frank Nielsen (Sony)
    Mohammad Emtiyaz Khan (RIKEN AIP)
    Mark Schmidt (UBC)

+Past interns of RIKEN AIP

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