Publications

International Conference Papers (Peer Reviewed)

1. Kentaro Kanamori, Ken Kobayashi, Takuya Takagi:
   "Learning Gradient Boosted Decision Trees with Algorithmic Recourse"
   In Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025), to appear.
   Acceptance rate: 5290 / 21575 = 24.5%.
   [code]

2. Kentaro Kanamori, Ken Kobayashi, Satoshi Hara, Takuya Takagi:
   "Algorithmic Recourse for Long-Term Improvement"
   In Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), to appear.
   Acceptance rate: 3260 / 12107 = 26.9%.
   [slide] [poster] [video] [code]

3. Taisei Tosaki, Eiichiro Uchino, Yohei Harada, Minoru Sakuragi, Yusuke Koyanagi, Seiji Okajima, Hirofumi Suzuki, Kentaro Kanamori, Masahiro Asaoka, Kouji Kurihara, Takuya Takagi, Koji Maruhashi, Yoshinori Tamada, Tatsuya Mikami, Koichi Murashita, Shigeyuki Nakaji, Yasushi Okuno:
   "Subgrouping Causal Networks of Disease Onset in Large-scale Health and Medical Data using Supercomputer Fugaku"
   In Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 4384-4391,  December 2024.

4. Shoki Yamao, Ken Kobayashi, Kentaro Kanamori, Takuya Takagi, Yuichi Ike, Kazuhide Nakata:
   "Distribution-Aligned Sequential Counterfactual Explanation with Local Outlier Factor"
   In Proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence (PRICAI 2024), pp. 243-256,  November 2024.
   Acceptance rate: 153 / 543 = 28.2%.

5. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
   "Learning Decision Trees and Forests with Algorithmic Recourse"
   In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pp. 22936-22962, Spotlight, July 2024.
   Acceptance rate: 2609 / 9473 = 27.5%.
   [PMLR] [arXiv] [slide] [poster] [code]

6. Kentaro Kanamori:
   "Learning Locally Interpretable Rule Ensemble"
   In Proceedings of the 2023 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023), pp. 360-377, September 2023. 
   Acceptance rate: 199 / 830 = 24.0%.
   [arXiv] [slide] [code]

7. Kota Mata, Kentaro Kanamori, Hiroki Arimura:
   "Computing the Collection of Good Models for Rule Lists"
   In Proceedings of the 17th International Conference on Machine Learning and Data Mining (MLDM 2022), pp. 151-165, July 2022.
   Acceptance rate: 33%.

8. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
   "Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees"
   In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), pp. 1846-1870, March 2022.
   Acceptance rate: 492 / 1685 = 29.2%.
   [slide] [poster] [video] [code]

9. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura:
   "Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization"
   In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021),  pp. 11564-11574,  May 2021.
   Acceptance rate: 1692 / 7911 = 21.4%.
   [slide] [poster] [video] [code]

10. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura:
    "DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization"
    In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), pp. 2855-2862, July 2020.
    Acceptance rate: 592 / 4717 = 12.6%.
    [slide] [poster] [video] [code]

11. Kentaro Kanamori, Satoshi Hara, Masakazu Ishihata, Hiroki Arimura:
    "Enumeration of Distinct Support Vectors for Interactive Decision Making"
    In Proceedings of the 2019 ICML Workshop on Human In the Loop Learning (HILL 2019), June 2019.

Journal Papers (Peer Reviewed)

1. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura:
   "Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization"
   Transactions of the Japanese Society for Artificial Intelligence,  vol. 36, no. 6, pp. C-L44_1-12. JSAI Best Paper Award 2021. 

2. Kentaro Kanamori, Hiroki Arimura:
   "Fairness-Aware Decision Tree Editing Based on Mixed-Integer Linear Optimization"
   Transactions of the Japanese Society for Artificial Intelligence,  vol. 36, no. 4, pp. B-L13_1-10.