Yoichi Chikahara

16 papers A* 2A 3Journal 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables. CoRR Yoichi Chikahara
2026	J	`jnl`	Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data. CoRR Yoichi Chikahara
2025	J	`jnl`	MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions. CoRR Hans Jarett J. Ong, Yoichi Chikahara, Tomoharu Iwata
2024	A	`conf`	Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation. UAI Yoichi Chikahara, Kansei Ushiyama
2024	J	`jnl`	Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation. CoRR Yoichi Chikahara, Kansei Ushiyama
2024	J	`jnl`	Meta-learning for heterogeneous treatment effect estimation with closed-form solvers. Mach. Learn. Tomoharu Iwata, Yoichi Chikahara
2024	A*	`conf`	Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model. AAAI Shunsuke Horii, Yoichi Chikahara
2023	J	`jnl`	Making individually fair predictions with causal pathways. Data Min. Knowl. Discov. Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima
2023	J	`jnl`	Meta-learning for heterogeneous treatment effect estimation with closed-form solvers. CoRR Tomoharu Iwata, Yoichi Chikahara
2023	J	`jnl`	Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model. CoRR Shunsuke Horii, Yoichi Chikahara
2022	J	`jnl`	Feature Selection for Discovering Distributional Treatment Effect Modifiers. CoRR Yoichi Chikahara, Makoto Yamada, Hisashi Kashima
2022	A	`conf`	Feature selection for discovering distributional treatment effect modifiers. UAI Yoichi Chikahara, Makoto Yamada, Hisashi Kashima
2021	A	`conf`	Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. AISTATS Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima
2020	J	`jnl`	Learning Individually Fair Classifier with Causal-Effect Constraint. CoRR Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino
2018	A*	`conf`	Causal Inference in Time Series via Supervised Learning. IJCAI Yoichi Chikahara, Akinori Fujino
2013	J	`jnl`	LibSBMLSim: a reference implementation of fully functional SBML simulator. Bioinform. Hiromu Takizawa, Kazushige Nakamura, Akito Tabira, Yoichi Chikahara, Tatsuhiro Matsui, Noriko Hiroi, Akira Funahashi