Mitsuhiro Fujikawa, Youhei Akimoto, Jun Sakuma, and Kazuto Fukuchi. Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift. The 28th International Conference on Artificial Intelligence and Statistics, 2025.

下坂 知広, 福地 一斗. 視覚言語モデルを用いたスプリアス相関の低減における欠損グループへの汎化. 第27回情報論的学習理論ワークショップ (at 情報論的学習理論ワークショップ), vol. IBIS2024, pp. -, 2024 (ポスターのみ).

Dai Shengitan, Akimoto Youhei, Jun Sakuma, and Fukuchi Kazuto. Poisoning Attack on Fairness of Fair Classification Algorithm through Threshold Control. 電子情報通信学会技術研究報告, vol. 123, 410, pp. 49-56, 2024.

永池 礼, 西山 大輝, 福地 一斗, 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, pages, 2024.

Kazuto Fukuchi and Jun Sakuma. Demographic Parity Constrained Minimax Optimal Regression under Linear Model. Advances in Neural Information Processing Systems, vol. 36, pp. 8653-8689, 2023.arXiv

大磯 秀幸, 福地 一斗, 秋本 洋平, 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.

藤川 光浩, 秋本 洋平, 佐久間 淳, 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, 311, pp. 58-65, 2023.

KAIWEN XU, 福地 一斗, 秋本 洋平, 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.

西山 大輝, 福地 一斗, 秋本 洋平, 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.

小路口 望, 福地 一斗, 秋本 洋平, 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.

大磯 秀幸, 福地 一斗, 秋本 洋平, 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.

藤川 光浩, 秋本 洋平, 佐久間 淳, 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. yes (at 情報論的学習理論ワークショップ), vol. IBIS2023, pp. -, 2023 (ポスターのみ).

Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, (IJCAI-23), , 8, pp. 519-526, 2023.

Thien Quang Tran, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Statistically Significant Pattern Mining with Ordinal Utility. IEEE Transactions on Knowledge and Data Engineering, vol. 35, 9, pp. 8770-8783, 2023.

Jiayang Liu, Weiming Zhang, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Unauthorized AI cannot recognize me: Reversible adversarial example. Pattern Recognition, vol. 134, 109048 pages, 2023.

Kazuya Kakizaki, Kazuto Fukuchi, and Jun Sakuma. Certified Defense for Content Based Image Retrieval. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4561-4570, 2023.

Thien Quang Tran, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation. The Thirty-Sixth AAAI Conference on Artificial Intelligence, vol. 36, 9, pp. 9614-9622, 2022.

Yu Zhe, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Domain Generalization via Adversarially Learned Novel Domains. IEEE Access, vol. 10, , pp. 101855-101868, 2022.

Yu Zhe, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Domain Generalization via Adversarial Learned Novel Domains. 2022 IEEE International Conference on Multimedia and Expo (ICME), 1–6 pages, 2022.

Hirofumi Syou, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Did You Use My GAN to Generate Fake? Post-hoc Attribution of GAN Generated Images via Latent Recovery. 2022 International Joint Conference on Neural Networks (IJCNN), 1–8 pages, 2022.

Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy. 2022 International Joint Conference on Neural Networks (IJCNN), 1–8 pages, 2022.

Kazuto Fukuchi and Jun Sakuma. Minimax Optimal Fair Regression under Linear Model. yes (at NeurIPS 2022 Workshop: Algorithmic Fairness through the Lens of Causality and Privacy), 2022.

Kazuto Fukuchi, Chia-Mu Yu, and Jun Sakuma. Locally Differentially Private Minimum Finding. IEICE Transactions on Information and Systems, vol. E105-D, 8, pp. 1418-1430, 2022.

Thien Quang Tran, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. Statistically Significant Pattern Mining with Ordinal Utility. KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discoveryand Data Mining, 1645–1655 pages, 2020.

Kazuto Fukuchi, Satoshi Hara, and Takanori Maehara. Faking Fairness via Stealthily Biased Sampling. The Thirty-Fourth AAAI Conference on Artificial Intelligence, Special Track on AI for Social Impact, vol. 34, 01, pp. 412-419, 2020.

Kazuto Fukuchi and Jun Sakuma. Minimax Optimal Additive Functional Estimation with Discrete Distribution: Slow Divergence Speed Case. 2018 IEEE International Symposium on Information Theory (ISIT), pp. 1041-1045, 2018.

Kazuya Kakizaki, Kazuto Fukuchi, and Jun Sakuma. Differentially Private Chi-squared Test by Unit Circle Mechanism. Proceedings of the 34th International Conference on Machine Learning, vol. 70, 1761–1770 pages, 2017.

Kazuto Fukuchi, Quang Khai Tran, and Jun Sakuma. Differentially Private Empirical Risk Minimization with Input Perturbation. Discovery Science, vol. 10558, 82–90 pages, 2017.

Kazuto Fukuchi and Jun Sakuma. Minimax optimal estimators for additive scalar functionals of discrete distributions. 2017 IEEE International Symposium on Information Theory (ISIT), , , pp. 2103-2107, 2017.

Rina Okada, Kazuto Fukuchi, and Jun Sakuma. Differentially Private Analysis of Outliers. Machine Learning and Knowledge Discovery in Databases, vol. 9285, 458–473 pages, 2015.

Kazuto Fukuchi, Toshihiro Kamishima, and Jun Sakuma. Prediction with Model-Based Neutrality. IEICE Transactions on Information and Systems, vol. E98.D, 8, pp. 1503-1516, 2015.

Kazuto Fukuchi and Jun Sakuma. Neutralized Empirical Risk Minimization with Generalization Neutrality Bound. Machine Learning and Knowledge Discovery in Databases, vol. 8724, 418–433 pages, 2014.

Kazuto Fukuchi, Jun Sakuma, and Toshihiro Kamishima. Prediction with Model-Based Neutrality. Machine Learning and Knowledge Discovery in Databases, vol. 8189, 499–514 pages, 2013.