| Making Tree Ensembles Interpretable S Hara, K Hayashi 2016 Workshop on Human Interpretability in Machine Learning, 81-85, 2016 | 62 | 2016 |
| Making tree ensembles interpretable: A Bayesian model selection approach S Hara, K Hayashi Proceedings of the 21th International Conference on Artificial Intelligence …, 2016 | 43 | 2016 |
| 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 | 42 | 2012 |
| Learning a common substructure of multiple graphical Gaussian models S Hara, T Washio Neural Networks 38, 23-38, 2012 | 34 | 2012 |
| Enumerate lasso solutions for feature selection S Hara, T Maehara Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 29 | 2017 |
| 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 | 28* | |
| Stationary subspace analysis as a generalized eigenvalue problem S Hara, Y Kawahara, T Washio, P Von Bünau International Conference on Neural Information Processing, 422-429, 2010 | 21 | 2010 |
| Data Cleansing for Models Trained with SGD S Hara, A Nitanda, T Maehara Advances in Neural Information Processing Systems 32 (NeurIPS'19), 2019 | 14 | 2019 |
| 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 | 11 | 2014 |
| Common substructure learning of multiple graphical gaussian models S Hara, T Washio Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 11 | 2011 |
| 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 | 10* | 2015 |
| Quantile regression approach to conditional mode estimation H Ota, K Kato, S Hara arXiv preprint arXiv:1811.05379, 2018 | 8 | 2018 |
| Approximate and exact enumeration of rule models S Hara, M Ishihata Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 7 | 2018 |
| Direct density ratio estimation with dimensionality reduction M Sugiyama, S Hara, P Von Bünau, T Suzuki, T Kanamori, M Kawanabe Proceedings of the 2010 SIAM International Conference on Data Mining, 595-606, 2010 | 7 | 2010 |
| Consistent and Efficient Nonparametric Different-Feature Selection ST Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Takafumi Ono, Ryo Okamoto Proceedings of the 20th International Conference on Artificial Intelligence …, 2017 | 6* | 2017 |
| Maximally invariant data perturbation as explanation S Hara, K Ikeno, T Soma, T Maehara arXiv preprint arXiv:1806.07004, 2018 | 5 | 2018 |
| Maximizing invariant data perturbation with stochastic optimization K Ikeno, S Hara arXiv preprint arXiv:1807.05077, 2018 | 4 | 2018 |
| Discounted average degree density metric and new algorithms for the densest subgraph problem H Yanagisawa, S Hara Networks 71 (1), 3-15, 2018 | 4 | 2018 |
| Quantum-state anomaly detection for arbitrary errors using a machine-learning technique S Hara, T Ono, R Okamoto, T Washio, S Takeuchi Physical Review A 94 (4), 042341, 2016 | 4 | 2016 |
| Predicting Halfway through Simulation: Early Scenario Evaluation Using Intermediate Features of Agent-Based Simulations Satoshi Hara, Rudy Raymond, Tetsuro Morimura, Hidemasa Muta Proceedings of the 2014 Winter Simulation Conference, 334-343, 2014 | 4 | 2014 |
