2024

1. D Shengitan , and Akimoto Youhei , J Sakuma and F Kazuto. Poisoning Attack on Fairness of Fair Classification Algorithm through Threshold Control. In 電子情報通信学会技術研究報告 , Vol.123 , pp.49-56 , 2024 . doi: .
2. 永池 礼 , 西山 大輝 , 秋本洋平 , 福地 一斗 and 佐久間 淳. マルチタスク学習における隠れたタスクに対する敵対的攻撃. , Vol.SCIS2024 , pp. , 2024 . doi: .

2023

1. 藤川 光浩 , 秋本 洋平 , 佐久間 淳 and 福地 一斗. neighbor-transfer-exponentを通した非絶対連続分布間の共変量シフト下での分類誤差解析. 電子情報通信学会技術研究報告 , Vol.123 , No.311 , pp.58-65 , 2023 .
2. K Fukuchi and J Sakuma. Demographic Parity Constrained Minimax Optimal Regression under Linear Model. In Advances in Neural Information Processing Systems , Vol.36 , pp.8653--8689 , 2023 .
3. 藤川 光浩 , 秋本 洋平 , 佐久間 淳 and 福地 一斗. neighbor-transfer-exponentを用いた非絶対連続分布間の転移学習の誤差解析. 情報論的学習理論ワークショップ , Vol.IBIS2023 , pp.- , 2023 .
4. 大磯 秀幸 , 福地 一斗 , 秋本 洋平 and 佐久間 淳. 物理的に実現可能な特徴をトリガーとしたクリーンラベルバックドア攻撃. コンピュータセキュリティシンポジウム論文集 , Vol.CSS2023 , pp.1219-1226 , 2023 . doi: .
5. T Q Tran , K Fukuchi , Y Akimoto and J Sakuma. Statistically Significant Pattern Mining with Ordinal Utility. IEEE Transactions on Knowledge and Data Engineering , Vol.35 , No.9 , pp.8770-8783 , 2023 . doi: 10.1109/TKDE.2022.3208626.
6. K Xu , K Fukuchi , Y Akimoto and J Sakuma. Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, (IJCAI-23) , Vol. , No.8 , pp.519-526 , 2023 . doi: 10.24963/IJCAI.2023/58. arXiv
7. 西山 大輝 , 福地 一斗 , 秋本 洋平 and 佐久間 淳. 精度劣化を伴わない特定クラスの再現率改善のための分類器学習. 人工知能学会全国大会論文集 , Vol.JSAI2023 , pp.3D1GS203-3D1GS203 , 2023 . doi: 10.11517/pjsai.JSAI2023.0_3D1GS203.
8. X U KAIWEN , 福地 一斗 , 秋本 洋平 and 佐久間 淳. ノックオフによる画像分類器の統計的有意なコンセプトに基づく説明. 人工知能学会全国大会論文集 , Vol.JSAI2023 , pp.4Q3OS1404-4Q3OS1404 , 2023 . doi: 10.11517/pjsai.JSAI2023.0_4Q3OS1404.
9. 大磯 秀幸 , 福地 一斗 , 秋本 洋平 and 佐久間 淳. コンセプトをトリガーとしたステルス性の高いバックドア攻撃. 人工知能学会全国大会論文集 , Vol.JSAI2023 , pp.3L1GS1103-3L1GS1103 , 2023 . doi: 10.11517/pjsai.JSAI2023.0_3L1GS1103.
10. 小路口 望 , 福地 一斗 , 秋本 洋平 and 佐久間 淳. 少数のセンシティブ属性を用いた公平な学習. 人工知能学会全国大会論文集 , Vol.JSAI2023 , pp.2D4GS205-2D4GS205 , 2023 . doi: 10.11517/pjsai.JSAI2023.0_2D4GS205.
11. J Liu , W Zhang , K Fukuchi , Y Akimoto and J Sakuma. Unauthorized AI cannot recognize me: Reversible adversarial example. Pattern Recognition , Vol.134 , pp.109048 , 2023 . doi: 10.1016/j.patcog.2022.109048.
12. K Kakizaki , K Fukuchi and J Sakuma. Certified Defense for Content Based Image Retrieval. In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pp.4561-4570 , 2023 .

2022

1. K Fukuchi and J Sakuma. Minimax Optimal Fair Regression under Linear Model. In NeurIPS 2022 Workshop: Algorithmic Fairness through the Lens of Causality and Privacy . arXiv
2. Y Zhe , K Fukuchi , Y Akimoto and J Sakuma. Domain Generalization via Adversarial Learned Novel Domains. In 2022 IEEE International Conference on Multimedia and Expo (ICME) , pp.1–6 , 2022 . doi: 10.1109/ICME52920.2022.9860025.
3. D Nishiyama , K Fukuchi , Y Akimoto and J Sakuma. CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy. In 2022 International Joint Conference on Neural Networks (IJCNN) , pp.1–8 , 2022 . doi: 10.1109/IJCNN55064.2022.9892108.
4. H Syou , K Fukuchi , Y Akimoto and J Sakuma. Did You Use My GAN to Generate Fake? Post-hoc Attribution of GAN Generated Images via Latent Recovery. In 2022 International Joint Conference on Neural Networks (IJCNN) , pp.1–8 , 2022 . doi: 10.1109/IJCNN55064.2022.9892704.
5. K Fukuchi , C-M Yu and J Sakuma. Locally Differentially Private Minimum Finding. IEICE Transactions on Information and Systems , Vol.E105-D , No.8 , pp.1418-1430 , 2022 . doi: 10.1587/transinf.2021EDP7187. arXiv
6. T Q Tran , K Fukuchi , Y Akimoto and J Sakuma. Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation. In The Thirty-Sixth AAAI Conference on Artificial Intelligence , Vol.36 , No.9 , pp.9614-9622 , 2022 . doi: 10.1609/aaai.v36i9.21195. arXiv
7. Y Zhe , K Fukuchi , Y Akimoto and J Sakuma. Domain Generalization via Adversarially Learned Novel Domains. IEEE Access , Vol.10 , No. , pp.101855-101868 , 2022 . doi: 10.1109/ACCESS.2022.3209815.

2020

1. T Q Tran , K Fukuchi , Y Akimoto and J Sakuma. Statistically Significant Pattern Mining with Ordinal Utility. In KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discoveryand Data Mining , pp.1645–1655 , 2020 . doi: 10.1145/3394486.3403215. arXiv
2. K Fukuchi , S Hara and T Maehara. Faking Fairness via Stealthily Biased Sampling. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, Special Track on AI for Social Impact , Vol.34 , No.01 , pp.412-419 , 2020 . doi: 10.1609/aaai.v34i01.5377. arXiv

2018

1. K Fukuchi and J Sakuma. Minimax Optimal Additive Functional Estimation with Discrete Distribution: Slow Divergence Speed Case. In 2018 IEEE International Symposium on Information Theory (ISIT) , pp.1041-1045 , 2018 . doi: 10.1109/ISIT.2018.8437725. arXiv

2017

1. K Fukuchi , Q K Tran and J Sakuma. Differentially Private Empirical Risk Minimization with Input Perturbation. In Discovery Science , Vol.10558 , pp.82–90 , 2017 . doi: 10.1007/978-3-319-67786-6_6. arXiv
2. K Kakizaki , K Fukuchi and J Sakuma. Differentially Private Chi-squared Test by Unit Circle Mechanism. In Proceedings of the 34th International Conference on Machine Learning , Vol.70 , pp.1761–1770 , 2017 .
3. K Fukuchi and J Sakuma. Minimax optimal estimators for additive scalar functionals of discrete distributions. In 2017 IEEE International Symposium on Information Theory (ISIT) , Vol. , No. , pp.2103-2107 , 2017 . doi: 10.1109/ISIT.2017.8006900. arXiv

2015

1. K Fukuchi , T Kamishima and J Sakuma. Prediction with Model-Based Neutrality. IEICE Transactions on Information and Systems , Vol.E98.D , No.8 , pp.1503-1516 , 2015 . doi: 10.1587/transinf.2014EDP7367.
2. R Okada , K Fukuchi and J Sakuma. Differentially Private Analysis of Outliers. In Machine Learning and Knowledge Discovery in Databases , Vol.9285 , pp.458–473 , 2015 . doi: 10.1007/978-3-319-23525-7_28. arXiv

2014

1. K Fukuchi and J Sakuma. Neutralized Empirical Risk Minimization with Generalization Neutrality Bound. In Machine Learning and Knowledge Discovery in Databases , Vol.8724 , pp.418–433 , 2014 . doi: 10.1007/978-3-662-44848-9_27. arXiv

2013

1. K Fukuchi , J Sakuma and T Kamishima. Prediction with Model-Based Neutrality. In Machine Learning and Knowledge Discovery in Databases , Vol.8189 , pp.499–514 , 2013 . doi: 10.1007/978-3-642-40991-2_32.