Center for Advanced Intelligence Project

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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