March 13, 2020 11:25

Ten papers were accepted at AISTATS 2020, a major conference on machine learning. For more details, please refer to the link below.

[Accepted Papers] https://www.aistats.org/accepted.html

  • RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
    Takashi Nicholas Maeda (RIKEN Center for Advanced Intelligence Project / Shiga University)
    Shohei Shimizu (RIKEN Center for Advanced Intelligence Project / Shiga University)
  • Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
    Nan Lu (The University of Tokyo)
    Tianyi Zhang (The University of Tokyo)
    Gang Niu (RIKEN)
    Masashi Sugiyama (RIKEN/ The University of Tokyo)
  • Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification
    Han Bao (The University of Tokyo / RIKEN)
    Masashi Sugiyama (RIKEN/ The University of Tokyo)
  • A Unified Statistically Efficient Estimation Framework for Unnormalized Models
    Masatoshi Uehara (Harvard University)
    Takafumi Kanamori (Tokyo Institute of Technology/ RIKEN AIP)
    Takashi Takenouchi (Future University Hakodate/ RIKEN Center for Advanced Intelligence Project)
    Takeru Matsuda (University of Tokyo/ RIKEN CBS)
  • Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees
    Atsushi Nitanda (The University of Tokyo/ RIKEN / JST PRESTO)
    Taiji Suzuki (The University of Tokyo/ RIKEN)
  • Understanding Generalization in Deep Learning via Tensor Methods
    Jingling Li (UMD)
    Yanchao Sun (University of Maryland/ College Park)
    Jiahao Su (UMD)
    Taiji Suzuki (The University of Tokyo/ RIKEN)
    Furong Huang (University of Maryland)
  • Sparse Hilbert-Schmidt Independence Criterion Regression
    Benjamin Poignard (Osaka University/ RIKEN AIP)
    Makoto Yamada (RIKEN AIP/ Kyoto University)
  • More Powerful Selective Kernel Tests for Feature Selection
    Jen Ning Lim (University College London)
    Makoto Yamada (RIKEN AIP/ Kyoto University)
    Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems)
    Yoshikazu Terada (Osaka University/ RIKEN)
    Shigeyuki Matsui (Nagoya University)
    Hidetoshi Shimodaira (Kyoto University/ RIKEN AIP)
  • On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
    Kohei Hayashi (Preferred Networks, Inc.)
    Masaaki Imaizumi (The Institute of Statistical Mathematics/ RIKEN AIP)
    Yuichi Yoshida (NII)
  • Stopping criterion for active learning based on deterministic generalization bounds
    Hideaki Ishibashi (Kyushu Institute of Technology)
    Hideitsu Hino (The Institute of Statistical Mathematics/ RIKEN AIP)

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