About me


Research interests

My central focus of research is in theoretical understanding of statistical machine learning, particularly from the following perspectives.

  1. Learning theory of loss functions: Loss functions are interesting because they characterize a large portion of task properties such as adversarial robustness (COLT2020) and imbalancedness (AISTATS2020, AISTATS2021). Some task properties can be attained simultaneously by proper losses (COLT2023).
  2. Evaluation metrics of predictions and representations. Recently, I am interested in how it is possible to learn good representations via similarity in light of a downstream task (ICML2018, AISTATS2022, ICML2022).

You may have a look at the slides of my past (and slightly outdated…) talks such as this to see my tastes.


News

🚨 リサーチアシスタント募集中

JSTさきがけ「未来数理科学」の研究課題「損失関数設計と最適化ダイナミクスの協調」に関連して、損失関数、最適化、ニューラルネットの帰納バイアス、表現学習に関する研究業務に携わっていただける方を募集しています。詳しくは直接ご連絡ください。

(older news)


Upcoming travels

  • Apr 24-30: Singapore (ICLR etc.)
  • May 2-5: Phuket (AISTATS)