June 2, 2020 13:57

18 papers were accepted at ICML 2020, a top conference on machine learning. For more details, please refer to the link below.

[Website] https://icml.cc/Conferences/2020
[Accepted Papers] https://icml.cc/Conferences/2020/AcceptedPapersInitial

  • Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
    Jingfeng Zhang (National University of Singapore)*
    Xu Xilie (Shandong University)
    Bo Han (HKBU / RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Lizhen Cui (ShanDong University)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
    Mohan Kankanhalli (National University of Singapore)
  • Variational Imitation Learning with Diverse-quality Demonstrations
    Voot Tangkaratt (RIKEN AIP)
    Bo Han (HKBU / RIKEN AIP)
    Mohammad Emtiyaz Khan (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • S2GA: Robust Deep Learning with Noisy Labels without Early Stopping
    Bo Han (HKBU / RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Xingrui Yu (University of Technology Sydney)
    Quanming Yao (4Paradigm)
    Miao Xu (University of Queensland/ RIKEN AIP)
    Ivor Tsang (University of Technology Sydney)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Online Dense Subgraph Discovery via Blurred-Graph Feedback
    Yuko Kuroki (The University of Tokyo / RIKEN AIP)
    Atsushi Miyauchi (University of Tokyo / RIKEN AIP)
    Junya Honda (University of Tokyo / RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Few-shot Domain Adaptation by Causal Mechanism Transfer
    Takeshi Teshima (The University of Tokyo / RIKEN AIP)
    Issei Sato (University of Tokyo / RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
    Yasutoshi Ida (NTT)
    Sekitoshi Kanai (NTT Software Innovation Center)
    Yasuhiro Fujiwara (NTT Communication Science Laboratories)
    Tomoharu Iwata (NTT)
    Koh Takeuchi (NTT)
    Hisashi Kashima (Kyoto University / RIKEN AIP)
  • Learning with Multiple Complementary Labels
    Lei Feng (Nanyang Technological University)*
    Takuo Kaneko (The University of Tokyo / RIKEN AIP)
    Bo Han (HKBU / RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Bo An (Nanyang Technological University)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Evolutionary Topology Search for Tensor Network Decomposition
    Chao Li (RIKEN AIP)
    Zhun Sun (RIKEN AIP)
  • Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
    Futoshi Futami (The University of Tokyo/ RIKEN AIP)
    Issei Sato (University of Tokyo / RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
    Yu-Ting Chou (National Taiwan University)*
    Gang Niu (RIKEN AIP)
    Hsuan-Tien Lin (National Taiwan University)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Searching to Exploit Memorization Effect in Learning from Noisy Labels
    Quanming Yao (4Paradigm)
    Hansi Yang (Tsinghua)
    Bo Han (HKBU / RIKEN AIP)
    Gang Niu (RIKEN AIP)
    James Kwok (Hong Kong University of Science and Technology)
  • Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
    Yusuke Tsuzuku (The University of Tokyo / RIKEN AIP)
    Issei Sato (University of Tokyo / RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Progressive Identification of True Labels for Partial-Label Learning
    Jiaqi Lv (Southeast University)*
    Miao Xu (University of Queensland/ RIKEN AIP)
    Lei Feng (Nanyang Technological University)*
    Gang Niu (RIKEN AIP)
    Xin Geng (Southeast University)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Graph Homomorphism Convolution
    Hoang Nguyen (RIKEN AIP)
    Takanori Maehara (RIKEN AIP)
  • Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
    Shion Takeno (Nagoya Institute of Technology)
    Hitoshi Fukuoka (Nagoya University)
    Yuhki Tsukada (Nagoya University)
    Toshiyuki Koyama (Nagoya University)
    Motoki Shiga (Gifu University)
    Ichiro Takeuchi (Nagoya Institute of Technology / RIKEN AIP)
    Masayuki Karasuyama (Nagoya Institute of Technology)
  • Do We Need Zero Training Loss After Achieving Zero Training Error?
    Takashi Ishida (The University of Tokyo / RIKEN AIP)
    Ikko Yamane (The University of Tokyo)
    Tomoya Sakai (NEC)
    Gang Niu (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
  • Handling the Positive-Definite Constraint in the Bayesian Learning Rule
    Wu Lin (UBC)+
    Mark Schmidt (University of British Columbia)
    Mohammad Emtiyaz Khan (RIKEN AIP)
  • Training Binary Neural Networks using the Bayesian Learning Rule
    Xiangming Meng (RIKEN AIP)
    Roman Bachmann (EPFL)*
    Mohammad Emtiyaz Khan (RIKEN AIP)
*They are past interns of RIKEN AIP.
+He is a past member of RIKEN-AIP. Now a PhD student who is cosupervised by a RIKEN PI.

Updated: June 3, 2020

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