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ORCIDSatoshi Hara 0001
Person information
- affiliation: University of Electro-Communications, Graduate School of Informatics and Engineering, Tokyo, Japan
- affiliation (former, PhD 2013): Osaka University, Graduate School of Engineering, Osaka, Japan
Other persons with the same name
- Satoshi Hara 0002 — Fuji Soft Inc., Japan
- Satoshi Hara 0003 — Mitsubishi Electric Corporation, Amagasaki, Japan
- Satoshi Hara 0004 — NIT Information and Communication Systems Laboratories, Japan
- Satoshi Hara 0005 — Fukuoka Institute of Technology, Department of Intelligent Mechanical Engineering, Fukuoka, Japan
Other persons with a similar name
- Emilio Satoshi Hara — Okayama University Graduate School of Medicine, Japan
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2020 – today
- 2025
[j7]Daisuke Hatano, Satoshi Hara, Hiromi Arai:
Path-Specific Counterfactual Fairness via Dividend Correction. Trans. Mach. Learn. Res. 2025 (2025)- [j6]Naoyuki Terashita, Satoshi Hara:
Variance Reduction of Stochastic Hypergradient Estimation by Mixed Fixed-Point Iteration. Trans. Mach. Learn. Res. 2025 (2025) - [j5]Naoyuki Terashita
, Hiroki Ohashi, Satoshi Hara:
Data Cleansing for GANs. IEEE Trans. Neural Networks Learn. Syst. 36(6): 11575-11588 (2025) - [c26]Kentaro Kanamori, Ken Kobayashi, Satoshi Hara, Takuya Takagi:
Algorithmic Recourse for Long-Term Improvement. ICML 2025 - [i14]Naoyuki Terashita, Hiroki Ohashi, Satoshi Hara:
Data Cleansing for GANs. CoRR abs/2504.00603 (2025) - 2024
- [j4]Satoshi Hara, Mitsuru Matsuura, Junya Honda, Shinji Ito:
Active model selection: A variance minimization approach. Mach. Learn. 113(11): 8327-8345 (2024) - [c25]Yudai Yamamoto, Satoshi Hara:
Fast Stealthily Biased Sampling Using Sliced Wasserstein Distance. ACML 2024: 873-888 - 2023
- [c24]Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Yuta Fujishige, Satoshi Hara:
Rule Mining for Correcting Classification Models. ICDM 2023: 1331-1336 - [c23]Satoshi Hara, Yuichi Yoshida:
Average Sensitivity of Decision Tree Learning. ICLR 2023 - [c22]Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh:
Fooling SHAP with Stealthily Biased Sampling. ICLR 2023 - [i13]Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Yuta Fujishige, Satoshi Hara:
Rule Mining for Correcting Classification Models. CoRR abs/2310.06446 (2023) - 2022
- [c21]Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Keisuke Goto, Yuta Fujishige, Satoshi Hara:
Explainable and Local Correction of Classification Models Using Decision Trees. AAAI 2022: 8404-8413 - [i12]Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh:
Fooling SHAP with Stealthily Biased Sampling. CoRR abs/2205.15419 (2022) - [i11]Naoyuki Terashita, Satoshi Hara:
Personalized Decentralized Bilevel Optimization over Stochastic and Directed Networks. CoRR abs/2210.02129 (2022) - 2021
- [c20]Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, Kentaro Inui:
Evaluation of Similarity-based Explanations. ICLR 2021 - [c19]Ulrich Aïvodji, Hiromi Arai, Sébastien Gambs, Satoshi Hara:
Characterizing the risk of fairwashing. NeurIPS 2021: 14822-14834 - [i10]Ulrich Aïvodji, Hiromi Arai, Sébastien Gambs, Satoshi Hara:
Characterizing the risk of fairwashing. CoRR abs/2106.07504 (2021) - 2020
- [c18]Kazuto Fukuchi, Satoshi Hara, Takanori Maehara:
Faking Fairness via Stealthily Biased Sampling. AAAI 2020: 412-419 - [c17]Danqing Pan, Tong Wang, Satoshi Hara:
Interpretable Companions for Black-Box Models. AISTATS 2020: 2444-2454 - [i9]Danqing Pan, Tong Wang, Satoshi Hara:
Interpretable Companions for Black-Box Models. CoRR abs/2002.03494 (2020) - [i8]Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, Kentaro Inui:
Evaluation Criteria for Instance-based Explanation. CoRR abs/2006.04528 (2020)
2010 – 2019
- 2019
- [c16]Satoshi Hara, Weichih Chen, Takashi Washio, Tetsuichi Wazawa, Takeharu Nagai:
SPoD-Net: Fast Recovery of Microscopic Images Using Learned ISTA. ACML 2019: 694-709 - [c15]Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp:
Fairwashing: the risk of rationalization. ICML 2019: 161-170 - [c14]Satoshi Hara, Atsushi Nitanda, Takanori Maehara:
Data Cleansing for Models Trained with SGD. NeurIPS 2019: 4215-4224 - [c13]Satoshi Hara, Takanori Maehara:
Convex Hull Approximation of Nearly Optimal Lasso Solutions. PRICAI (2) 2019: 350-363 - [i7]Kazuto Fukuchi, Satoshi Hara, Takanori Maehara:
Pretending Fair Decisions via Stealthily Biased Sampling. CoRR abs/1901.08291 (2019) - [i6]Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp:
Fairwashing: the risk of rationalization. CoRR abs/1901.09749 (2019) - [i5]Kentaro Kanamori, Satoshi Hara, Masakazu Ishihata, Hiroki Arimura:
Enumeration of Distinct Support Vectors for Interactive Decision Making. CoRR abs/1906.01876 (2019) - [i4]Satoshi Hara, Atsushi Nitanda, Takanori Maehara:
Data Cleansing for Models Trained with SGD. CoRR abs/1906.08473 (2019) - 2018
- [j3]Hiroki Yanagisawa, Satoshi Hara:
Discounted average degree density metric and new algorithms for the densest subgraph problem. Networks 71(1): 3-15 (2018) - [c12]Satoshi Hara, Masakazu Ishihata:
Approximate and Exact Enumeration of Rule Models. AAAI 2018: 3157-3164 - [c11]Satoshi Hara, Kohei Hayashi:
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. AISTATS 2018: 77-85 - [i3]Satoshi Hara, Kouichi Ikeno, Tasuku Soma, Takanori Maehara:
Maximally Invariant Data Perturbation as Explanation. CoRR abs/1806.07004 (2018) - [i2]Kouichi Ikeno, Satoshi Hara:
Maximizing Invariant Data Perturbation with Stochastic Optimization. CoRR abs/1807.05077 (2018) - [i1]Satoshi Hara, Takanori Maehara:
Convex Hull Approximation of Nearly Optimal Lasso Solutions. CoRR abs/1810.05992 (2018) - 2017
- [c10]Satoshi Hara, Takanori Maehara:
Enumerate Lasso Solutions for Feature Selection. AAAI 2017: 1985-1991 - [c9]Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Takafumi Ono, Ryo Okamoto, Shigeki Takeuchi:
Consistent and Efficient Nonparametric Different-Feature Selection. AISTATS 2017: 130-138 - 2015
- [c8]Satoshi Hara, Tetsuro Morimura, Toshihiro Takahashi, Hiroki Yanagisawa, Taiji Suzuki:
A Consistent Method for Graph Based Anomaly Localization. AISTATS 2015 - 2014
- [c7]Satoshi Hara, Rudy Raymond, Tetsuro Morimura, Hidemasa Muta:
Predicting halfway through simulation: early scenario evaluation using intermediate features of agent-based simulations. WSC 2014: 334-343 - [c6]Hidemasa Muta, Rudy Raymond, Satoshi Hara, Tetsuro Morimura:
A multi-objective genetic algorithm using intermediate features of simulations. WSC 2014: 793-804 - 2013
- [j2]Satoshi Hara, Takashi Washio:
Learning a common substructure of multiple graphical Gaussian models. Neural Networks 38: 23-38 (2013) - 2012
- [j1]Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau, Terumasa Tokunaga, Kiyohumi Yumoto:
Separation of stationary and non-stationary sources with a generalized eigenvalue problem. Neural Networks 33: 7-20 (2012) - [c5]Satoshi Hara, Takashi Washio:
Anomalous Neighborhood Selection. ICDM Workshops 2012: 474-480 - [c4]Satoshi Hara, Takashi Washio:
Group Sparse Inverse Covariance Selection with a Dual Augmented Lagrangian Method. ICONIP (3) 2012: 108-115 - 2011
- [c3]Satoshi Hara, Takashi Washio:
Common Substructure Learning of Multiple Graphical Gaussian Models. ECML/PKDD (2) 2011: 1-16 - 2010
- [c2]Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau:
Stationary Subspace Analysis as a Generalized Eigenvalue Problem. ICONIP (1) 2010: 422-429 - [c1]Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe:
Direct Density Ratio Estimation with Dimensionality Reduction. SDM 2010: 595-606
Coauthor Index
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