| Fairwashing: the risk of rationalization U Aïvodji, H Arai, O Fortineau, S Gambs, S Hara, A Tapp Proceedings of the 36th International Conference on Machine Learning (ICML …, 0 | 159* | |
| Making tree ensembles interpretable: A Bayesian model selection approach S Hara, K Hayashi Proceedings of the 21th International Conference on Artificial Intelligence …, 2016 | 137 | 2016 |
| Making Tree Ensembles Interpretable S Hara, K Hayashi 2016 Workshop on Human Interpretability in Machine Learning, 81-85, 2016 | 109 | 2016 |
| Data Cleansing for Models Trained with SGD S Hara, A Nitanda, T Maehara Advances in Neural Information Processing Systems 32 (NeurIPS'19), 2019 | 71 | 2019 |
| Separation of stationary and non-stationary sources with a generalized eigenvalue problem S Hara, Y Kawahara, T Washio, P Von BüNau, T Tokunaga, K Yumoto Neural networks 33, 7-20, 2012 | 65 | 2012 |
| Enumerate lasso solutions for feature selection S Hara, T Maehara Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 63 | 2017 |
| Quantile regression approach to conditional mode estimation H Ota, K Kato, S Hara | 56 | 2019 |
| Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+ 2] annulation sequence M Kondo, HDP Wathsala, M Sako, Y Hanatani, K Ishikawa, S Hara, ... Chemical communications 56 (8), 1259-1262, 2020 | 52 | 2020 |
| Evaluation of similarity-based explanations K Hanawa, S Yokoi, S Hara, K Inui arXiv preprint arXiv:2006.04528, 2020 | 43 | 2020 |
| Learning a common substructure of multiple graphical Gaussian models S Hara, T Washio Neural Networks 38, 23-38, 2012 | 40 | 2012 |
| Characterizing the risk of fairwashing U Aïvodji, H Arai, S Gambs, S Hara Advances in Neural Information Processing Systems 34, 14822-14834, 2021 | 25 | 2021 |
| Stationary subspace analysis as a generalized eigenvalue problem S Hara, Y Kawahara, T Washio, P Von Bünau Neural Information Processing. Theory and Algorithms: 17th International …, 2010 | 23 | 2010 |
| Energy-, time-, and labor-saving synthesis of α-ketiminophosphonates: Machine-learning-assisted simultaneous multiparameter screening for electrochemical oxidation M Kondo, A Sugizaki, MI Khalid, HDP Wathsala, K Ishikawa, S Hara, ... Green Chemistry 23 (16), 5825-5831, 2021 | 22 | 2021 |
| Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber T Kojima, T Washio, S Hara, M Koishi Scientific Reports 10 (1), 18127, 2020 | 18 | 2020 |
| Anomaly Detection in Reconstructed Quantum States Using a Machine-Learning Technique Satoshi Hara, Takafumi Ono, Ryo Okamoto, Takashi Washio, Shigeki Takeuchi Physical Review A 89 (2), 022104, 2014 | 17 | 2014 |
| Bayesian optimization-driven parallel-screening of multiple parameters for the flow synthesis of biaryl compounds M Kondo, HDP Wathsala, MSH Salem, K Ishikawa, S Hara, T Takaai, ... Communications Chemistry 5 (1), 148, 2022 | 16 | 2022 |
| Interpretable companions for black-box models D Pan, T Wang, S Hara International conference on artificial intelligence and statistics, 2444-2454, 2020 | 16 | 2020 |
| Faking Fairness via Stealthily Biased Sampling K Fukuchi, S Hara, T Maehara arXiv preprint arXiv:1901.08291, 2019 | 15* | 2019 |
| Approximate and exact enumeration of rule models S Hara, M Ishihata Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 15 | 2018 |
| A Consistent Method for Graph Based Anomaly Localization Satoshi Hara, Tetsuro Morimura, Toshihiro Takahashi, Hiroki Yanagisawa ... Proceedings of the Eighteenth International Conference on Artificial …, 2015 | 13* | 2015 |
