2018/9/7 17:55

機械学習のトップカンファレンスであるNIPS2018において、AIPセンターから12本の論文が採択されました。

[NIPS 2018] https://nips.cc/Conferences/2018/Schedule

 

  • #1 Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
    Yusuke Tsuzuku (The University of Tokyo)
    Issei Sato (The university of Tokyo/ RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/ The University of Tokyo)
  • #2 Binary Classification from Positive-Confidence Data (spotlight)
    Takashi Ishida (University of Tokyo/ RIKEN AIP)
    Gang Niu (RIKEN AIP)
    Masashi Sugiyama (RIKEN AIP/ The University of Tokyo)
  • #3 Uplift Modeling from Separate Labels
    Ikko Yamane (The University of Tokyo/ RIKEN AIP)
    Florian Yger (Université Paris-Dauphine)
    Jamal Atif (Université Paris-Dauphine)
    Masashi Sugiyama (RIKEN AIP/ The University of Tokyo)
  • #4  Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
    Motoya Ohnishi (Keio University/ KTH Royal Institute of Technology/ RIKEN AIP)
    Masahiro Yukawa (Keio University)
    Mikael Johansson (KTH Royal Institute of Technology)
    Masashi Sugiyama (RIKEN AIP/ The University of Tokyo)
  • #5 Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels
    Bo Han (RIKEN AIP/ University of Technology Sydney)
    Quanming Yao (4Paradigm)
    Xingrui Yu (University of Technology Sydney)
    Gang Niu (RIKEN AIP)
    Miao Xu (RIKEN AIP)
    Weihua Hu (The University of Tokyo/ RIKEN AIP)
    Ivor Tsang (University of Technology Sydney)
    Masashi Sugiyama (RIKEN AIP/ The University of Tokyo)
  • #6 Masking: A New Perspective of Noisy Supervision
    Bo Han (RIKEN AIP/ University of Technology Sydney)
    Jiangchao Yao (Cooperative Medianet Innovation Center/ Shang hai Jiao Tong University)
    Gang Niu (RIKEN AIP)
    Mingyuan Zhou (University of Texas at Austin)
    Ivor Tsang (University of Technology Sydney)
    Ya Zhang (Cooperative Medianet Innovation Center/ Shang hai Jiao Tong University)
    Masashi Sugiyama (RIKEN AIP/ The University of Tokyo)
  • #7 Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
    Tomoya Murata (NTT DATA Mathematical Systems Inc.)
    Taiji Suzuki (The University of Tokyo/ RIKEN AIP)
  • #8 Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams.
    Tam Le (RIKEN AIP)
    Makoto Yamada (Kyoto University/ RIKEN AIP)
  • #9 Metric on Nonlinear Dynamical Systems with Koopman Operators
    Isao Ishikawa (RIKEN AIP)
    Keisuke Fujii (RIKEN AIP)
    Masahiro Ikeda (RIKEN AIP)
    Yuka Hashimoto (Keio University)
    Yoshinobu Kawahara (Osaka University/ RIKEN AIP)
  • #10 Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
    Shinji Ito (NEC Corporation)
    Daisuke Hatano (RIKEN AIP)
    Sumita Hanna (Tokyo Metropolitan University)
    Akihiro Yabe (NEC Corporation)
    Takuro Fukunaga (RIKEN AIP/ JST PRESTO)
    Naonori Kakimura (Keio University)
    Ken-ichi Kawarabayashi (National Institute of Informatics)
  • #11 Legendre Decomposition for Tensors
    Mahito Sugiyama (NII)
    Hiroyuki Nakahara (RIKEN CBS)
    Koji Tsuda (University of Tokyo/ RIKEN AIP)
  • #12 SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
    Aaron Mishkin (UBC)
    Frederik Kunstner (EPFL)
    Didrik Nielsen (RIKEN AIP)
    Mark Schmidt (UBC)
    Mohammad Emtiyaz Khan (RIKEN AIP)

 

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