Stationary Subspace Analysis as a Generalized Eigenvalue Problem. - Satoshi Hara 001; Yoshinobu Kawahara; Takashi Washio; Paul von Bünau (ICONIP (1), 2010)

Direct Density Ratio Estimation with Dimensionality Reduction. - Masashi Sugiyama; Satoshi Hara 001; Paul von Bünau; Taiji Suzuki; Takafumi Kanamori; Motoaki Kawanabe (SDM, 2010)

Common Substructure Learning of Multiple Graphical Gaussian Models. - Satoshi Hara 001; Takashi Washio (ECML/PKDD (2), 2011)

Anomalous Neighborhood Selection. - Satoshi Hara 001; Takashi Washio (ICDM Workshops, 2012)

Group Sparse Inverse Covariance Selection with a Dual Augmented Lagrangian Method. - Satoshi Hara 001; Takashi Washio (ICONIP (3), 2012)

Separation of stationary and non-stationary sources with a generalized eigenvalue problem. - Satoshi Hara 001; Yoshinobu Kawahara; Takashi Washio; Paul von Bünau; Terumasa Tokunaga; Kiyohumi Yumoto (Neural Networks, 2012)

Learning a common substructure of multiple graphical Gaussian models. - Satoshi Hara 001; Takashi Washio (Neural Networks, 2013)

Predicting halfway through simulation: early scenario evaluation using intermediate features of agent-based simulations. - Satoshi Hara 001; Rudy Raymond; Tetsuro Morimura; Hidemasa Muta (WSC, 2014)

A multi-objective genetic algorithm using intermediate features of simulations. - Hidemasa Muta; Rudy Raymond; Satoshi Hara 001; Tetsuro Morimura (WSC, 2014)

A Consistent Method for Graph Based Anomaly Localization. - Satoshi Hara 001; Tetsuro Morimura; Toshihiro Takahashi; Hiroki Yanagisawa; Taiji Suzuki (AISTATS, 2015)

Enumerate Lasso Solutions for Feature Selection. - Satoshi Hara 001; Takanori Maehara (AAAI, 2017)

Consistent and Efficient Nonparametric Different-Feature Selection. - Satoshi Hara 001; Takayuki Katsuki; Hiroki Yanagisawa; Takafumi Ono; Ryo Okamoto; Shigeki Takeuchi (AISTATS, 2017)

Approximate and Exact Enumeration of Rule Models. - Satoshi Hara 001; Masakazu Ishihata (AAAI, 2018)

Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. - Satoshi Hara 001; Kohei Hayashi (AISTATS, 2018)

Maximally Invariant Data Perturbation as Explanation. - Satoshi Hara 001; Kouichi Ikeno; Tasuku Soma; Takanori Maehara (CoRR, 2018)

Maximizing Invariant Data Perturbation with Stochastic Optimization. - Kouichi Ikeno; Satoshi Hara 001 (CoRR, 2018)

Discounted average degree density metric and new algorithms for the densest subgraph problem. - Hiroki Yanagisawa; Satoshi Hara 001 (Networks, 2018)

SPoD-Net: Fast Recovery of Microscopic Images Using Learned ISTA. - Satoshi Hara 001; Weichih Chen; Takashi Washio; Tetsuichi Wazawa; Takeharu Nagai (ACML, 2019)

Fairwashing: the risk of rationalization. - Ulrich Aïvodji; Hiromi Arai; Olivier Fortineau; Sébastien Gambs; Satoshi Hara 001; Alain Tapp (ICML, 2019)

Data Cleansing for Models Trained with SGD. - Satoshi Hara 001; Atsushi Nitanda; Takanori Maehara (NeurIPS, 2019)

Convex Hull Approximation of Nearly Optimal Lasso Solutions. - Satoshi Hara 001; Takanori Maehara (PRICAI (2), 2019)

Pretending Fair Decisions via Stealthily Biased Sampling. - Kazuto Fukuchi; Satoshi Hara 001; Takanori Maehara (CoRR, 2019)

Enumeration of Distinct Support Vectors for Interactive Decision Making. - Kentaro Kanamori; Satoshi Hara 001; Masakazu Ishihata; Hiroki Arimura (CoRR, 2019)

Faking Fairness via Stealthily Biased Sampling. - Kazuto Fukuchi; Satoshi Hara 001; Takanori Maehara (AAAI, 2020)

Interpretable Companions for Black-Box Models. - Danqing Pan; Tong Wang; Satoshi Hara 001 (AISTATS, 2020)

Evaluation Criteria for Instance-based Explanation. - Kazuaki Hanawa; Sho Yokoi; Satoshi Hara 001; Kentaro Inui (CoRR, 2020)

Evaluation of Similarity-based Explanations. - Kazuaki Hanawa; Sho Yokoi; Satoshi Hara 001; Kentaro Inui (ICLR, 2021)

Characterizing the risk of fairwashing. - Ulrich Aïvodji; Hiromi Arai; Sébastien Gambs; Satoshi Hara 001 (NeurIPS, 2021)

Explainable and Local Correction of Classification Models Using Decision Trees. - Hirofumi Suzuki; Hiroaki Iwashita; Takuya Takagi; Keisuke Goto 001; Yuta Fujishige; Satoshi Hara 001 (AAAI, 2022)

Personalized Decentralized Bilevel Optimization over Stochastic and Directed Networks. - Naoyuki Terashita; Satoshi Hara 001 (CoRR, 2022)

Rule Mining for Correcting Classification Models. - Hirofumi Suzuki; Hiroaki Iwashita; Takuya Takagi; Yuta Fujishige; Satoshi Hara 001 (ICDM, 2023)

Average Sensitivity of Decision Tree Learning. - Satoshi Hara 001; Yuichi Yoshida (ICLR, 2023)

Fooling SHAP with Stealthily Biased Sampling. - Gabriel Laberge; Ulrich Aïvodji; Satoshi Hara 001; Mario Marchand; Foutse Khomh (ICLR, 2023)

Fast Stealthily Biased Sampling Using Sliced Wasserstein Distance. - Yudai Yamamoto; Satoshi Hara 001 (ACML, 2024)

Active model selection: A variance minimization approach. - Satoshi Hara 001; Mitsuru Matsuura; Junya Honda; Shinji Ito (Mach. Learn., 2024)

Path-Specific Counterfactual Fairness via Dividend Correction. - Daisuke Hatano; Satoshi Hara 001; Hiromi Arai (Trans. Mach. Learn. Res., 2025)

Variance Reduction of Stochastic Hypergradient Estimation by Mixed Fixed-Point Iteration. - Naoyuki Terashita; Satoshi Hara 001 (Trans. Mach. Learn. Res., 2025)

Data Cleansing for GANs. - Naoyuki Terashita; Hiroki Ohashi; Satoshi Hara 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)