Skip to content

Publications

Refereed Papers and Proceedings

  1. H. Moriyama, J. Wu, H. Watanabe, D. Ochi, H. Kawata, S. Kanai, D. Yokota, R.D. Niklai, K. Kobayashi, and K. Nakata: Prediction performance-based grouping for bubble-based federated learning in retail demand forecasting. Proceedings of the 23rd Pacific Rim International Conference on Artificial Intelligence (Short Paper), (2026), accepted.
  2. S. Yamao, K. Kobayashi, R. Matsui, S. Nagai, N. Nishimura, and K. Nakata:Robust decision-focused learning via worst-case regret minimization. Proceedings of 42nd Annual Conference on Uncertainty in Artificial Intelligence, to appear.
  3. S. Aikawa, A. Suzuki, K. Yoshitake, K. Teshigawara, A. Iwabuchi, K. Kobayashi, and K. Nakata:Hierarchical time series forecasting with robust reconciliation. Transactions on Machine Learning Research, (2026). Open Review   Preprint
  4. K. Yoshida, K. Kobayashi, K. Kawai, Y. Ito, N. Ikemoto, and K. Nakata: The electric vehicle routing problem with hard time windows and nonlinear charging and discharging. Proceedings of the 15th International Conference on Operations Research and Enterprise Systems, (2025), 155--166. DOI
  5. S. Yamao, Y. Mibuchi, K. Yoshida, J. Wu, Y. Nakagawa, Y. Nakaya, K. Kobayashi, and K. Nakata: Robust prescriptive pricing under competitor price uncertainty. Proceedings of the 2025 IEEE International Conference on Big Data (Short Paper), (2025), 2274–2281. DOI
  6. K. Toyoda, Y. Utsumi, K. Kobayashi, and K. Nakata: Classification of strategic patents under the scarcity of labeled data. Proceedings of the 2025 IEEE International Conference on Big Data on Big Data Industry and Government Program (Short Paper), (2025), 2658–2662. DOI
  7. K. Kanamori, K. Kobayashi, and T. Takagi: Learning gradient boosted decision trees with algorithmic recourse. Proceedings of the 39th Annual Conference on Neural Information Processing Systems, (2025). Open Review
  8. S. Yamao, R. Ueda, S. Koguchi, M. Nakase, A. Suzuki, K. Toyoda, K. Kobayashi, and K. Nakata: Estimating sales transitions between competing products via optimal transport. PLOS ONE, 20 (2025), e0325173. DOI
  9. K. Kanamori, K. Kobayashi, S. Hara, and T. Takagi: Algorithmic recourse for long-term improvement. Proceedings of the 42nd International Conference on Machine Learning, (2025). Open Review
  10. A. Sannai, Y. Hikima, K.Kobayashi, A.Tanaka, and N. Hamada: Bézier flow: a surface-wise gradient descent method for multi-objective optimization. Transactions on Machine Learning Research, (2025). Open Review
  11. A. Inoue, B. Zhu, K. Mizutani, K. Kobayashi, T. Yasuda, A. Wellner, C. C. Liu, and T. Kitaguchi: Prediction of single-mutation effects for fluorescent immunosensor engineering with an end-to-end trained protein language model. JACS Au, (2025), DOI: 10.1021/jacsau.4c01189. DOI
  12. Y. Hikima, K. Kobayashi, A. Tanaka, A. Sannai, and N. Hamada: Stochastic gradient descent for Bézier simplex representation of Pareto set in multi-objective optimization. Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, (2025). Open Review
  13. S. Koguchi, K. Nakata, K. Kobayashi, K. Kawakami, T. Nakajima, and K. Kratzer: Online joint optimization of sponsored search ad bid amounts and product prices on E-commerce. Proceedings of the 14th International Conference on Operations Research and Enterprise Systems, (2025), 67--78. DOI
  14. A. Suzuki, K. Kobayashi, K. Nakata, Y. Kurume, N. Sawasaki, and Y. Sasamoto: Decision diagram optimization for allocating patients to medical diagnosis. Operations Research 2024 Proceedings, (2025), 406--411. DOI
  15. N. Nishimura, K. Kobayashi, and K. Nakata: Balancing immediate revenue and future off-policy evaluation in coupon allocation. Proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, (2024), 422--428. DOI   Preprint
  16. S. Yamao, K. Kobayashi, K. Kanamori, T. Takagi, Y. Ike, and K. Nakata: Distribution-aligned sequential counterfactual explanation with local outlier factor. Proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, (2024), 243--256. DOI
  17. K. Kanamori, T. Takagi, K. Kobayashi, and Y. Ike: Learning decision trees and forests with algorithmic recourse. Proceedings of the 41st International Conference on Machine Learning, PMLR 235 (2024), 22936--22962. PDF   Preprint
  18. H. Kiyohara, R. Kishimoto, K. Kawakami, K. Kobayashi, K. Nakata, and Y. Saito: Towards assessing and benchmarking risk-return tradeoff of off-policy evaluation. Proceedings of the International Conference on Learning Representations, (2024). Open Review   Preprint
  19. A. Ueta, M. Tanaka, K. Kobayashi, and K. Nakata: Inverse-optimization-based uncertainty set for robust linear optimization. Operations Research 2023 Proceedings, (2025), 527–-533. DOI   Preprint
  20. K. Mizutani, A. Ueta, R. Ueda, R. Oishi, T. Hara, Y. Hoshino, K. Kobayashi, and K. Nakata: Zero-inflated Poisson tensor factorization for sparse purchase data in E-commerce markets. Proceedings of the 11th International Conference on Industrial Engineering and Applications (Europe), (2024), 158--171. DOI
  21. M. Higashi, M. Sung, D. Yamane, K. Inamuro, S. Nagai, K. Kobayashi, and K. Nakata: Decision tree clustering for time series data: an approach for enhanced interpretability and efficiency. Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence, (2023), 457--468. DOI
  22. K. Kobayashi, Y. Takano, and K. Nakata: Cardinality-constrained distributionally robust portfolio optimization. European Journal of Operational Research, 309 (2023), 1173--1182. DOI   Preprint
  23. R. Tanabe, Y. Akimoto, K. Kobayashi, H. Umeki, S. Shirakawa, and N. Hamada: A two-phase framework with a Bézier simplex-based interpolation method for computationally expensive multi-objective optimization. Proceedings of ACM Genetic and Evolutionary Computation Conference, (2022), 601--610. DOI   Preprint
  24. K. Kanamori, T. Takagi, K. Kobayashi, and Y. Ike: Counterfactual explanation trees: transparent and consistent actionable recourse with decision trees. Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, PMLR 151 (2022), 1846--1870.
    PDF
  25. K. Kanamori, T. Takagi, K. Kobayashi, and H. Arimura: Distribution-aware counterfactual explanation by mixed-integer linear optimization. Transactions of the Japanese Society for Artificial Intelligence, 36 (2021), C-L44_1--12. DOI
  26. K. Kobayashi, Y. Takano, and K. Nakata: Bilevel cutting-plane algorithm for solving cardinality-constrained mean-CVaR portfolio optimization. Journal of Global Optimization, 81 (2021), 493--528. DOI   Preprint
  27. K. Kanamori, T. Takagi, K. Kobayashi, Y. Ike, K. Uemura, and H. Arimura: Ordered counterfactual explanation by mixed-integer linear optimization. Proceedings of the 35th AAAI Conference on Artificial Intelligence, 35 (2021), 11564--11574.
    DOI   Preprint
  28. T. Shiratori, K. Kobayashi, and Y. Takano: Prediction of hierarchical time series using structured regularization and its application to artificial neural networks. PLOS ONE, 15 (2020), e0242099. DOI   Preprint
  29. K. Kanamori, T. Takagi, K. Kobayashi, and H. Arimura: DACE: Distribution-aware counterfactual explanation by mixed-integer linear optimization. Proceedings of the 29th International Joint Conference on Artificial Intelligence, 29 (2020), 2855--2862.
    DOI   Preprint
  30. A. Tanaka, A. Sannai, K. Kobayashi, and N. Hamada: Asymptotic risk of Bézier simplex fitting. Proceedings of the 34th AAAI Conference on Artificial Intelligence, 34 (2020), 2416--2424.
    DOI   Preprint
  31. K. Kobayashi and Y. Takano: A branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems. Computational Optimization and Applications, 75 (2020), 493--513. DOI   Preprint
  32. L. Sun, X. Yu, L. Wang, J. Sun, H. Inakoshi, K. Kobayashi, and H. Kobashi: Automatic neural network search method for open set recognition. The 26th IEEE International Conference on Image Processing, 26 (2019), 4090--4094. DOI
  33. 西村直樹, 小林健, 吉住宗朔: 制約つき比例ハザードモデルを用いたヘアサロンの再来店状況分析. オペレーションズ・リサーチ, 64 (2019), 65--72. PDF
  34. K. Kobayashi, N. Hamada, A. Sannai, A. Tanaka, K. Bannai, and M. Sugiyama: Bézier simplex fitting: describing Pareto fronts of simplicial problems with small samples in multi-objective optimization. Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 33 (2019), 2304--2313.
    DOI   Preprint
  35. R. Tamura, K. Kobayashi, Y. Takano, R. Miyashiro, K. Nakata, and T. Matsui: Mixed integer quadratic optimization formulations for eliminating multicollinearity based on variance inflation factor. Journal of Global Optimization, 73 (2019), 431--446. DOI   Preprint
  36. R. Tamura, K. Kobayashi, Y. Takano, R. Miyashiro, K. Nakata, and T. Matsui: Best subset selection for eliminating multicollinearity. Journal of the Operations Research Society of Japan, 60 (2017), 321--336. DOI
  37. 高野祐一, 田中未来, 鮏川矩義, 神里栄, 竹山光将, 千代竜佑, 小林健, 田中研太郎, 中田和秀: ファジィクラスタワイズ回帰を用いた共同購入型クーポンサイトの閲覧傾向分析. オペレーションズ・リサーチ, 59 (2014), 81--87. PDF

Preprints

  1. R. Ueda, T. Sato, K. Kobayashi, and K. Nakata: Interior-point vanishing problem in semidefinite relaxations for neural network verification. arXiv preprint, arXiv:2506.10269 (2025).
  2. H. Kiyohara, R. Kishimoto, K. Kawakami, K. Kobayashi, K. Nakata, and Y. Saito: SCOPE-RL: a Python library for offline reinforcement learning and off-policy evaluation. arXiv preprint, arXiv:2311.18206 (2023).
  3. K. Kanamori, T. Takagi, K. Kobayashi, and Y. Ike: Counterfactual explanation with missing values. arXiv preprint, arXiv:2304.14606 (2023).
  4. A. Tanaka, A. Sannai, K. Kobayashi, and N. Hamada: Approximate Bayesian computation of Bézier simplices. arXiv preprint, arXiv:2104.04679 (2021).

Refereed Workshop Proceedings

  1. S. Nagai, R. Inaba, R. Oishi, S. Aikawa, Y. Mibuchi, H. Moriyama, K. Kobayashi, and K. Nakata: Zero-shot demand forecasting for products with limited sales periods. The 7th Workshop on Big Data for Economic and Business Forecasting, Workshop at 2024 IEEE International Conference on Big Data, (2024). DOI
  2. K. Oh, N. Nishimura, M. Sung, K. Kobayashi, and K. Nakata: An IPW-based unbiased ranking metric in two-sided markets. Causal Inference and Machine Learning in Practice, Workshop at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (2023). Preprint
  3. M. Kajitani, K. Kobayashi, Y. Ike, T. Yamanashi, Y. Umeda, Y. Kadooka, and G. Shinozaki: Application of topological data analysis to delirium detection. Topological Data Analysis and Beyond, Workshop at the 34th Annual Conference on Neural Information Processing Systems 2020, (2020). PDF

Non-Refereed Papers and Articles

  1. 小林健: 混合整数半正定値最適化問題に対する切除平面法とその周辺. 第 34 回 RAMP 数理最適化シンポジウム論文集, (2022), 75--88.
  2. 田村隆太, 小林健, 高野祐一, 宮代隆平, 中田和秀, 松井知己: 多重共線性を考慮した回帰式の変数選択問題の定式化. オペレーションズ・リサーチ, 63 (2018), 128--133. PDF
  3. 山根智之, 菅原光太郎, 西村直樹, 小林健, 吉田佑輔, 高野祐一, 中田和秀: 時系列モデルによる商品販促効果の分析. オペレーションズ・リサーチ, 61 (2016), 65--70. PDF
  4. 小林健, 高野祐一, 宮代隆平, 中田和秀: 多重共線性を考慮した回帰式の変数選択--混合整数半正定値計画法を用いた解法--. 京都大学数理解析研究所講究録 1931 最適化アルゴリズムの進展・理論・応用・実装, (2014), 169--183. PDF

Others

  1. 小林健: 機械学習の反実仮想説明と混合整数最適化. オペレーションズ・リサーチ, 69 (2024), 143--150. PDF
  2. 小林健: 基数制約つき平均・分散モデルに対する切除平面法. オペレーションズ・リサーチ, 67 (2022), 360--365. PDF
  3. 小林健, 岩永二郎, 田中未来: 2021年春季企業事例交流会ルポ (第46回). オペレーションズ・リサーチ, 66 (2021), 548--550. PDF
  4. 小林健: 半正定値最適化問題に対する切除平面法と混合整数半正定値最適化問題への拡張. オペレーションズ・リサーチ, 65 (2020), 656--661. PDF
  5. 小林健: 2018年春季企業事例交流会ルポ (第41回). オペレーションズ・リサーチ, 63 (2018), 507--508. PDF
  6. 小林健: 多重共線性を考慮した回帰式の変数選択問題に対する混合整数計画法を用いた厳密解法 (学生論文賞受賞論文要約). オペレーションズ・リサーチ, 60 (2015), 752--734. PDF