January 27, 2023 10:01

Seve papers have been accepted at AISTATS 2023, a major conference on machine learning. (As of January 27, 2023)

[Accepted Papers]

  • A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets

Hideaki Ishibashi (Kyushu Institute of Technology)
Masayuki Karasuyama (Nagoya Institute of Technology/RIKEN AIP)
Ichiro Takeuchi (Nagoya University/RIKEN AIP)
Hideitsu Hino (ISM/RIKEN AIP)

  • Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data

Hiroshi Morioka (RIKEN-AIP)
Aapo Hyvärinen (University of Helsinki)

  • Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits

Taira Tsuchiya (Kyoto University / RIKEN)
Shinji Ito (NEC)
Junya Honda (Kyoto University / RIKEN)

  • Nyström Method for Accurate and Scalable Implicit Differentiation

Ryuichiro Hataya (RIKEN ADSP/RIKEN AIP)
Makoto Yamada (OIST/Kyoto University/RIKEN AIP)

  • The Lie-Group Bayesian Learning Rule

Mehmet Eren Kiral(RIKEN AIP)
Thomas Möllenhoff (RIKEN AIP)
Mohammad Emtiyaz Khan (RIKEN AIP)

  • Learning in RKHM: a C*-algebraic twist for kernel machines

Yuka Hashimoto (NTT / RIKEN-AIP)
Masahiro Ikeda (RIKEN-AIP / Keio University)
Hachem Kadri (Aix-Marseille University)

  • Scalable Unbalanced Sobolev Transport for Measures on a Graph

Truyen Nguyen* (Onto Innovation)
Kenji Fukumizu (ISM)
(*: equal contribution)

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