研究成果

論文・プロシーディングス

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• 査読付き論文
• 査読付き国際会議プロシーディングス
• プレプリント・テクニカルレポート・講究録
• 解説記事

査読付き論文

• S. Aikawa, A. Suzuki, K. Yoshitake, K. Teshigawara, A. Iwabuchi, K. Kobayashi, K. Nakata: Hierarchical time series forecasting with robust reconciliation. Transactions on Machine Learning Research, 2026.
• 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: e0325173 (2025).
• 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, 5 (2025), 955-964.
• T. Hara, Y. Sumiya, and K. Nakata: Trend analysis with interpretability and cold-start problems for recommender systems. The Review of Socionetwork Strategies, 18 (2024), 329–344.
• M. Nishijima: On the longest chain of faces of the completely positive and copositive cones. Linear Algebra and its Applications, 698 (2024), 479–491.
• K. Majima, K. Kawakami, K. Ishizuka, and K. Nakata: Keyword-level bayesian online bid optimization for sponsored search advertising. Operations Research Forum, 5 (2024).
• M. Nishijima and K. Nakata: Generalizations of doubly nonnegative cones and their comparison. Journal of the Operations Research Society of Japan, 67 (2024), 84–109.
• M. Nishijima and K. Nakata: Approximation hierarchies for copositive cone over symmetric cone and their comparison. Journal of Global Optimization, 88 (2024), 831–870.
• Y. Ma, S. Sengoku, and K. Nakata: The realized local volatility surface. RISK Journals: Journal of Investment Strategies, 12 (2023), 1–21.
• 東将己, 山根大輝, 原朋史, 梅津大雅, 馬嶋海斗, 松井諒生, 中田和秀: 育児Q&Aサイトにおける質問の時系列を考慮した複数の子供の月齢予測. オペレーションズ・リサーチ, 68 (2023), 75–84.
• Y. Hoshino, Y. Utsumi, Y. Matsuda, Y. Tanaka, and K. Nakata: IPC prediction of patent documents using neural network with attention for hierarchical structure. PLOS ONE, 18: e0282361 (2023).
• K. Kobayashi, Y. Takano, and K. Nakata: Cardinality-constrained distributionally robust portfolio optimization. European Journal of Operational Research, 309 (2023), 1173–1182.
• R. Matsui, S. Yaginuma, T. Naito, and K. Nakata: Noise-robust sampling for collaborative metric learning. The Review of Socionetwork Strategies, 16 (2022), 307–332.
• M. Kuramata, R. Katsuki, and K. Nakata: Solving large break minimization problems in a mirrored double round-robin tournament using quantum annealing. PLOS ONE, 17: e0266846 (2022).
• K. Ishizuka, K. Kurogi, K. Kawakami, D. Iwai, and K. Nakata: Generating search text ads from keywords and landing pages via BERT2BERT. Annual Conference of Japanese Society of Artificial Intelligence, 1423 (2021), 27–33.
• K. Kobayashi, Y. Takano, and K. Nakata : Bilevel cutting-plane algorithm for solving cardinality-constrained mean-CVaR portfolio optimization problems. Journal of Global Optimization, 81 (2021), 493–528.
• K. Kawakami, H. Kobayashi, and K. Nakata : Seasonal inventory management model for raw materials in steel industry. INFORMS Journal on Applied Analytics, 51 (2021), 312–324.
• M. Nishijima and K. Nakata : A block coordinate descent method for sensor network localization. Optimization Letters, 16 (2022), 1051–1071.
• 岩田真奈, 桑原淳, 石塚湖太, 倉又迪哉, 清原明加, 中田和秀: タクシーの流し営業における強化学習を用いた顧客獲得ナビ. オペレーションズ・リサーチ, 66 (2021), 75–83.
• 松井諒生, 住谷有規, 笹尾知広, 中田和秀: 反実仮想機械学習を用いたタクシーの乗車数予測と配置最適化. オペレーションズ・リサーチ, 66 (2021), 66–74.
• 土橋諒太, 陳晨, 三浦真和, 中田和秀: 自然言語処理的アプローチによるテレビ視聴データの解析. オペレーションズ・リサーチ, 65 (2020), 85–92.
• 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.
• 高正妍, 田澤浩二, チョウイ, 大原靖之, 山野上勇人, 桑原惇, 片山翔太, 中田和秀: Ensemble LDAを用いた既存および新規顧客へのスタイリスト推薦. オペレーションズ・リサーチ, 64 (2019), 95–101 .
• 福永峻, 田村悠, 根市和旗, 市瀬将也, 小槙瑠理子, 花村鴻太郎, 戸田開人, 片山翔太, 中田和秀: 部分再帰型ニューラルネットワークを用いたヘアサロンチェーンにおける顧客の離脱予測. オペレーションズ・リサーチ, 64 (2019), 87–94 .
• 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, 70 (2019), 431–446.
• 田村悠, 吉住宗朔, 福永峻, 三宅聡一郎, 片山翔太, 中田和秀: ファッションECサイトにおけるアンケートを用いたブランド推薦システム. オペレーションズ・リサーチ, 63 (2018), 91–98 .
• 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.
• 田澤浩二, 吉住宗朔, 平野豪一, 片山翔太, 中田和秀: 待ち行列シミュレータを用いた区役所窓口における最適な職員数の提案. オペレーションズ・リサーチ, 62 (2017), 75–82.
• 志甫有真, 谷川奈穂, 馬場隆, 菊地宏治, 片山翔太, 高野祐一, 中田和秀: 複数の販売チャネルでの購入を促進するための商品推薦手法. 情報科学研究, 36 (2016), 1–10.
• M. Tanaka and K. Nakata: Successive projection method for well-conditioned matrix approximation problems. IEEE Signal Processing Letters, 21 (2014), 418–422.
• 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.
• M. Tanaka and K. Nakata: Positive definite matrix approximation with condition number constraint. Optimization Letters. 8 (2014), 939–947.
• N. Sukegawa and A. Miyauchi: A note on the complexity of the maximum edge clique partitioning problem with respect to clique number. Discrete Optimization, 10 (2013), 331–332.
• A. Miyauchi and Y. Miyamoto: Computing an upper bound of modularity. European Physical Journal B, 86 (2013), 302.
• M. Tanaka, K. Nakata, and H. Waki: Numerical reduction method for doubly nonnegative optimization problems. Journal of Math-for-Industry, 5 (2013), 41–50.
• M. Tanaka, K. Nakata, and H. Waki: Application of a facial reduction algorithm and an inexact primal-dual path-following method for doubly nonnegative relaxation for mixed binary nonconvex quadratic optimization problems. Pacific Journal of Optimization, 8 (2012), 699–724.

査読付き国際会議プロシーディングス

• S. Yamao, K. Kobayashi, R. Matsui, S. Nagai, N. Nishimura, K. Nakata: Robust Decision-Focused Learning via Worst-Case Regret Minimization, Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, 2026.
• 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, (2026).
• 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 2025 IEEE International Conference on Big Data, (2025).
• K. Toyoda, Y. Utsumi, K. Kobayashi, K. Nakata: Classification of strategic patents under the scarcity of labeled data, 2025 IEEE International Conference on Big Data, (2025).
• 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 (NeurIPS 2025), (2025).
• K. Kanamori, K. Kobayashi, S. Hara, and T. Takagi: Algorithmic recourse for long-term improvement. Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), (2025).
• 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 (AISTATS 2025), (2025).
• 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 (ICORES 2025), (2024).
• 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. Proceedings of 2024 IEEE International Conference on Big Data (BigData 2024), (2024).
• A. Suzuki, K. Kobayashi, K. Nakata, Y. Kurume, N. Sawasaki, and Y. Sasamoto: Decision diagram optimization for allocating patients to medical diagnosis. Operations Research Proceedings 2024 Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), (2024).
• 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 21th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2024), (2024).
• 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 (ICML 2024), (2024).
• 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 (ICLR 2024), (2024).
• 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 (ICIEA 2024), (2024).
• A. Ueta, M. Tanaka, K. Kobayashi, and K. Nakata: Inverse-optimization-based uncertainty set for robust linear optimization. Operations Research Proceedings 2023 Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), (2023).
• T. Hara, Y. Sumiya, and K. Nakata: Temporal positive collective matrix factorization for interpretable trend analysis in recommender systems. Proceedings of 2023 IEEE International Conference on Data Mining (ICDM 2023), (2023).
• 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 (PRICAI 2023), (2023).
• M. Higashi, Y. Utsumi, and K. Nakata: Patent classification for business strategy with BERT. Proceedings of Intelligent Computing and Optimization (ICO 2023), 6 (2023).
• Y. Hoshino, M. Tasaki, K. Mizutani, M. Azami, K. Ishizuka, and K. Nakata: Predicting response probability by embedding questions in online question recommendation. Proceedings of Web Intelligence and Intelligence Agent Technology (WI-IAT 2022), 21 (2022).
• Y. Hoshino, R. Matsui, K. Ishizuka, K. Ishikawa, T. Umetsu, and K. Nakata: Hierarchical Bayesian recommendation model for inter-company collaboration using cross-industry questionnaire. Proceedings of 2022 IEEE 9th International Conference on Industrial Engineering and Applications (ICIEA 2022), 9 (2022).
• R. Matsui, S. Yaginuma, T. Naito, and K. Nakata: Confident collaborative metric learning. Proceedings of IEEE International Workshop on Data Mining for Service (DMS 2021), (2021), 246–253.
• A. Watanabe, M. Kuramata, K. Majima, H. Kiyohara, K. Kondo, and K. Nakata: Constrained generalized additive 2 model with consideration of high-order interactions. Proceedings of International Conference on Electrical, Computer and Energy Technologies (ICECET 2021), (2021), 1–6.
• M. Nishijima and Y. Liu : Native language identification and reconstruction of native language relationship using Japanese learner corpus. Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation, 35 (2021), 364–372.
• K. Ishizuka and K. Nakata: Text mining for factor modeling of Japanese stock performance. Proceedings of 2021 IEEE 8th International Conference on Industrial Engineering and Applications, 8 (2021), 538–542.
• M. Kuramata, R. Katsuki, and K. Nakata: Larger sparse quadratic assignment problem optimization using quantum annealing and a bit-flip heuristic algorithm. Proceedings of 2021 IEEE 8th International Conference on Industrial Engineering and Applications (PACLIC 2021), 8 (2021), 556–565.
• Y. Saito, S. Yaginuma, Y. Nishino, H. Sakata, and K. Nakata: Unbiased recommender learning from missing-not-at-random implicit feedback. Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM 2020), 13 (2020), 501–509.
• Y. Saito, H. Sakata, and K. Nakata: Cost-effective and stable policy optimization algorithm for uplift modeling with multiple treatments. Proceedings of the 2020 SIAM International Conference on Data Mining (ICDM 2020), (2020), 406–414.
• K. Kawakami and M. Tanaka: Ship routing problem with berthing time clash avoidance constraints and minimizing demurrage. Proceedings of the Tenth Triennial Symposium on Transportation Analysis (TRISTAN 2019), (2019), 454–457.
• M. Iwata, K. Otake, and T. Namatame: Analysis of the characteristics of customer defection on a hair salon considering individual differences. Proceedings of International Conference on Human-Computer Interaction, 11579 (2019), 378–391.
• Y. Saito, H. Sakata, and K. Nakata: Doubly robust prediction and evaluation methods improve uplift modeling for observational data. Proceedings of the SIAM International Conference on Data Mining (ICDM 2019), 19 (2019), 468–476.

プレプリント・テクニカルレポート・講究録

• H. Kiyohara, R. Kishimoto, K. Kawakami, K. Kobayashi, K. Nakata, and Y. Saito: Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation. arXiv preprint, arXiv: 2311.18207, (2024).
• M. Nishijima and B. F. Lourenço: Non-facial exposedness of copositive cones over symmetric cones. Optimization Online, (2024).
• M. Nishijima: On the longest chain of faces of the completely positive and copositive cones. Optimization Online, (2023).
• 星野雄毅, 内海祥雅, 中田和秀: Contrastive Learningを利用した類似特許検索. Jxiv preprint, Jxiv: 344, (2023).
• M. Nishijima and K. Nakata: Approximation hierarchies for copositive cone over symmetric cone and their comparison. arXiv preprint, arXiv: 2211.12753, (2022).
• M. Nishijima and K. Nakata: Generalization of doubly nonnegative cone: focusing on inner-approximation for generalized copositive cone. arXiv preprint, arXiv: 2204.12119, (2022).
• K. Ishikawa and K. Nakata: Online trading models with deep reinforcement learning in the forex market considering transaction costs. arXiv preprint, arXiv: 2106.03035, (2021).
• M. Iwata, Y. Matsuda, Y. Utsumi, Y. Tanaka, and K. Nakata: Technical progress analysis using a dynamic topic model for technical terms to revise patent classification codes. arXiv preprint, arXiv: 2012.10120, (2020).
• K. Tazawa, K. Neichi, Y. Ohara, K. Chikuma, S. Katayama, and K. Nakata: Apparel item recommendation using graph regularized nonnegative tensor factorization. Technical Report 2017-5, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2017).
• 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.
• 廣瀬貴也, 鈴木翔太, 佐藤悠介, 鈴木寛人, 中田和秀: 実務で現れるスタッフスケジューリングに対する近似解法. 京都大学数理解析研究所講究録 1981 新時代を担う最適化: モデル化手法と数値計算, (2015), 98–116.
• A. Karam, A. Eltawil, T. Mizutani, K. Nakata, and N.A. Harraz: Quay crane allocation problem with the internal truck capacity constraint in container terminals. 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), 94–106.
• 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.
• T. Matsui, N. Sukegawa, and A. Miyauchi: Fractional programming formulation for the vertex coloring problem. arXiv preprint, arXiv: 1402.5769 (2014).
• A. Miyauchi and N. Sukegawa: Maximizing Barber’s bipartite modularity is also hard. arXiv preprint, arXiv: 1310.4656 (2013).
• M. Tanaka and K. Nakata: Successive projection method for well-conditioned matrix approximation problems. Technical Report 2013-12, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2013).
• K. Kobayashi and M. Tanaka: Perspective reformulation for ship navigation problem. Technical Report 2013-10, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2013).
• M. Tanaka and K. Kobayashi: MISOCP formulation and route generation algorithm for ship navigation problem. Technical Report 2013-8, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2013).
• N. Sukegawa and A. Miyauchi: A note on the complexity of the maximum edge clique partitioning problem with respect to clique number. Technical Report 2013-6, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2013).
• A. Miyauchi and N. Sukegawa: Redundant constraints in the standard formulation for the clique partitioning problem. Technical Report 2013-5, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2013).
• 田中未来, 中田和秀: 条件数制約つき正定値行列近似問題について. 京都大学数理解析研究所講究録 1829 最適化手法の理論と応用の繋がり, (2013), 149–155.
• 宮内敦史, 鮏川矩義: クリーク分割問題に対する疎な定式化. 京都大学数理解析研究所講究録 1829 最適化手法の理論と応用の繋がり, (2013), 113–121.
• M. Tanaka, K. Nakata, and H. Waki: Numerical reduction method for doubly nonnegative optimization problems. Technical Report 2013-2, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2013).
• 田中未来, 中田和秀, 脇隼人: 高度な意思決定問題に対するソリューション技術確立のための錐最適化手法の研究. 高度科学技術社会リスク・ソリューション 2012 論文集, (2013), 246–261.
• M. Tanaka and K. Nakata: Positive definite matrix approximation with condition number constraint. Technical Report 2012-6, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2012).
• 田中未来, 中田和秀, 脇隼人: 0-1整数変数を含む非凸2次最適化問題の非負半正定値緩和に対する面的縮小と効率的解法. 京都大学数理解析研究所講究録 1773 最適化手法の深化と広がり, (2012), 186–197.
• M. Tanaka, K. Nakata, and H. Waki: Application of a facial reduction algorithm and an inexact primal-dual interior-point method for doubly nonnegative relaxation for mixed binary nonconvex quadratic optimization problems. Technical Report 2011-11, Department of Industrial Engineering and Management, Tokyo Institute of Technology, (2011).

解説記事

• 倉又迪哉, 中田和秀: 量子アニーリングと組合せ最適化. オペレーションズ・リサーチ, 67 (2022), 280–289.

• 中田和秀: 経営工学によるスマート社会の実現を目指して　ー東京工業大学　中田研究室ー. 経営システム, Vol. 30, No.1, 43-47 (2020).

• 中田和秀: 主双対内点法. オペレーションズ・リサーチ, Vol.64, No.4, 218-224 (2019).

• 中田和秀: データ解析コンペティションへの挑戦. オペレーションズ・リサーチ, Vol.63, No.5, 274-277 (2018).

• 中田和秀: 半正定値計画の問題記述＆解決能力. オペレーションズ・リサーチ, Vol.55, 387-392, (2010).

• 中田和秀, 藤澤克樹, 福田光浩, 山下真, 中田真秀, 小林和博: 最適化ソフトウェアSDPA. 応用数理 18, 2-14, (2008)．