Research field (3)：
Applied mathematics and statistics
, Basic mathematics
, Intelligent informatics
Research keywords (3)：
画像処理
, 信号処理
, 機械学習
Research theme for competitive and other funds (5)：
2022 - 2026 日常生活動作の予測に基づく居宅介護ケアプランの最適化手法の確立
2021 - 2025 個別化医療の適応的臨床研究を支える統計・機械学習法に関する研究
2020 - 2024 Research and development of nonlinear Selective Inference for high-dimensional and small number of samples data
2016 - 2021 個別化医療の開発のための統計的方法論の構築とその実践に関する総合的研究
2016 - 2018 Developing Nonlinear Feature Selection Algorithm for Ultra High-Dimensional Data
Papers (108)：
Yoichi Chikahara, Makoto Yamada, Hisashi Kashima. Feature selection for discovering distributional treatment effect modifiers. Uncertainty in Artificial Intelligence(UAI). 2022. 400-410
Ayato Toyokuni, Sho Yokoi, Hisashi Kashima, Makoto Yamada. Computationally Efficient Wasserstein Loss for Structured Labels. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop. 2021. 1-7
Ayato Toyokuni, Sho Yokoi, Hisashi Kashima, Makoto Yamada. Computationally Efficient Wasserstein Loss for Structured Labels. CoRR. 2021. abs/2103.00899
Sugiyama Masashi, Yamada Makoto, du Plessis Marthinus Christoffel, Liu Song. Learning under Non-Stationarity: Covariate Shift Adaptation, Class-Balance Change Adaptation, and Change Detection. Journal of the Japan Statistical Society, Japanese Issue. 2014. 44. 1. 113-136
Masashi Sugiyama, Makoto Yamada, Marthinus Christoffel du Plessis. Learning under nonstationarity: Covariate shift and class-balance change. Wiley Interdisciplinary Reviews: Computational Statistics. 2013. 5. 6. 465-477
