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研究業績

March 27, 202313 min read

2024年度

論文

  1. Takehiro Habara, Ryosuke Kojima, Floor-Field-Guided Neural Model for Crowd Counting, IEEE Access, 2024.
  2. Akihiro Takemura, Katsumi Inoue, Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming, Theory and Practice of Logic Programming, 2024
  3. Kengo Nakamura, Masaaki Nishino, Shuhei Denzumi. Single Family Algebra Operation on BDDs and ZDDs Leads to Exponential Blow-Up, 35th International Symposium on Algorithms and Computation (ISAAC 2024), 2024.
  4. Yin Jun Phua, Katsumi Inoue, Variable Assignment Invariant Neural Networks for Learning Logic Programs, 18th International conference on Neural-Symbolic Learning and Reasoning (NeSy 2024), 2024
  5. Mingyue Liu, Ryo Ueda, Zhen Wan, Katsumi Inoue, Chris Willcocks, Neuro-symbolic Contrastive Learning for Cross-domain Inference, 40th International Conference on Logic Programming (ICLP 2024), 2024.
  6. Akihiro Takemura, Katsumi Inoue, Differentiable Logic Programming for Distant Supervision, The 27th European Conference on Artificial Intelligence (ECAI 2024), 2024.
  7. Meghyn Bienvenu, Katsumi Inoue, Daniil Kozhemiachenko, Abductive reasoning in a paraconsistent framework, 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024), 2024.
  8. Irumi Sugimori, Katsumi Inoue, Hidetomo Nabeshima, Torsten Schaub, Takehide Soh, Naoyuki Tamura, Mutsunori Banbara, Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set Programming, 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024), 2024.
  9. Kun Gao, Katsumi Inoue, Yongzhi Cao, Hanpin Wang, A differentiable first-order rule learner for inductive logic programming, 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), Journal Track, 2024.
  10. Tony Ribeiro, Maxime Folschette, Morgan Magnin, Kotaro Okazaki, Lo Kuo-Yen, Antoine Roquilly, Jérémie Poschmann, Katsumi Inoue, Learning From Interpretation Transitions with Unknowns, 4th International Joint Conference on Learning & Reasoning, (IJCLR 2024), 2024.
  11. Irumi Sugimori, Katsumi Inoue, Hidetomo Nabeshima, Torsten Schaub, Takehide Soh, Naoyuki Tamura and Mutsunori Banbara, ASP-based Large Neighborhood Prioritized Search for Course Timetabling, 17th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR 2024), 2024.
  12. Tuan Nguyen Quoc, Katsumi Inoue and Chiaki Sakama, Linear Algebraic Partial Evaluation of Logic Programs, The 36th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2024), 2024
  13. Ryuichi Kanoh, Mahito Sugiyama, Neural Tangent Kernels for Axis-Aligned Tree Ensembles, The 41st International Conference on Machine Learning (ICML 2024), 2024.
  14. Masaaki Nishino, Kengo Nakamura, Norihito Yasuda, Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error, The 41st International Conference on Machine Learning (ICML 2024), 2024.
  15. Kun Gao, Katsumi Inoue, Yongzhi Cao, Hanpin Wang, A differentiable first-order rule learner for inductive logic programming, Artificial Intelligence Journal (AIJ), 2024.
  16. Profir-Petru Pârțachi, Mahito Sugiyama, Bringing Structure to Naturalness: On the Naturalness of ASTs, 46th International Conference on Software Engineering (ICSE 2024) Posters Track, 2024.

招待講演

  1. Katsumi Inoue, Linear Algebraic Approaches to Logic Programming, 40th International Conference on Logic Programming (ICLP 2024), 2024.
  2. Katsumi Inoue, Algebraic Logic Programming and Learning, The 4th International Joint Conference on Learning and Reasoning (IJCLR 2024), 2024.
  3. Katsumi Inoue, Algebraic Connection between Logic Programming and Machine Learning, The 17th International Symposium on Functional and Logic Programming (FLOPS 2024), 2024.
  4. Masaaki Nishino, Decision Diagrams and its Applications, The 6th Workshop on Enumeration Problems and Applications (WEPA 2024), 2024.
  5. 杉山 麿人, 「記号推論に接続する機械学習」への幾何学的アプローチ, AFSA 2024年度第2回領域集会, 2024.

受賞

  1. Best Student Paper Award 2024 at the 17th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR 2024), Irumi Sugimori, Katsumi Inoue, Hidetomo Nabeshima, Torsten Schaub, Takehide Soh, Naoyuki Tamura and Mutsunori Banbara, "ASP-based Large Neighborhood Prioritized Search for Course Timetabling", 2024.
  2. Best Poster Award at Technical AI Safety 2024, Alex F. Spies, Michael I. Ivanitskiy, Katsumi Inoue and other 9 co-authors, "Structured Representations in Maze-Solving Transformers", 2024.
  3. IBIS2024優秀プレゼンテーション賞ファイナリスト, 加納龍一, 杉山麿人, "決定木アンサンブルにおけるLinear Mode Connectivityの達成", 2024.

書籍・総説

  1. Masatsugu Yamada, Mahito Sugiyama, Primer on Graph Machine Learning, in: Hiroko Satoh, Kimito Funatsu, Hiroshi Yamamoto (ed.), Drug Development Supported by Informatics, Springer, 2024.
  2. Masatsugu Yamada, Mahito Sugiyama, Subgraph-Based Molecular Graph Generation, in: Hiroko Satoh, Kimito Funatsu, Hiroshi Yamamoto (ed.), Drug Development Supported by Informatics, Springer, 2024.

2023年度

論文

  1. Nicolas Schwind, Katsumi Inoue, Sébastien Konieczny, Pierre Marquis, BeliefFlow: A Framework for Logic-Based Belief Diffusion via Iterated Belief Change, 38th AAAI Conference on Artificial Intelligence (AAAI-24), 2024.
  2. Yuya Yamada, Mutsunori Banbara, Katsumi Inoue, Torsten Schaub, Ryuhei Uehara, Combinatorial Reconfiguration with Answer Set Programming: Algorithms, Encodings, and Empirical Analysis, The 18th International Conference and Workshops on Algorithms and Computation (WALCOM 2024), 2024.
  3. Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara, Many-body Approximation for Non-negative Tensors, Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), 2023.
  4. Michael Ivanitskiy, Alex Spies et al. Linearly Structured World Representations in Maze-Solving Transformers, NeurIPS 2023 UniReps Workshop, 2023.
  5. Kiyotaka Matsue, Mahito Sugiyama, Unsupervised Tensor Based Feature Extraction from Multivariate Time Series, IEEE Access, vol. 11, pp. 116277-116295, 2023.
  6. Takeru Isobe, Katsumi Inoue, Learning Strategies of Inductive Logic Programming Using Reinforcement Learning, Proceeding of the 32nd International Conference on Inductive Logic Programming (ILP 2023), 2023.
  7. Sota Moriyama, Koji Watanabe, Katsumi Inoue, GNN Based Extraction of Minimal Unsatisfiable Subsets, Proceeding of the 32nd International Conference on Inductive Logic Programming (ILP 2023), 2023.
  8. Takahiro Hirate, Mutsunori Banbara, Katsumi Inoue, Xiao-Nan Lu, Hidetomo Nabeshima, Torsten Schaub, Takehide Soh, Naoyuki Tamura, Hamiltonian Cycle Reconfiguration with Answer Set Programming, Proceedings of the 18th European Conference on Logics in Artificial Intelligence (JELIA 2023), 2023.
  9. Yuya Yamada, Mutsunori Banbara, Katsumi Inoue, Torsten Schaub, recongo: Bounded Combinatorial Reconfiguration with Answer Set Programming, Proceedings of the 18th European Conference on Logics in Artificial Intelligence (JELIA 2023), 2023.
  10. Mitsuhiro Odaka, Morgan Magnin, Katsumi Inoue, Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways, Frontiers in Genetics, 2023.
  11. Taisuke Sato, Katsumi Inoue, Differentiable learning of matricized DNFs and its application to Boolean networks, Machine Learning, 2023.
  12. Koji Watanabe, Katsumi Inoue, Learning State Transition Rules from High-Dimensional Time Series Data with Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machines, Human-Centric Intelligent Systems, 2023.
  13. Nicolas Schwind, Emir Demirović, Katsumi Inoue, Jean-Marie Lagniez, Algorithms for partially robust team formation, Autonomous Agents and Multi-Agent Systems, 2023.
  14. Kazu Ghalamkari, Mahito Sugiyama, Energy-Based Non-Negative Tensor Factorization via Multi-Body Modeling, ICML 2023 Workshop Duality Principles for Modern Machine Learning, 2023.
  15. Ryuichi Kanoh, Mahito Sugiyama, Investigating Axis-Aligned Differentiable Trees through Neural Tangent Kernels, ICML 2023 Workshop Differentiable Almost Everything, 2023.
  16. Masatsugu Yamada, Mahito Sugiyama, How Graph Features from Message Passing Affect Graph Classification and Regression?, Intelligent Data Analysis, 2023.
  17. Masatsugu Yamada, Mahito Sugiyama, Molecular Graph Generation by Decomposition and Reassembling, ACS Omega, 2023.
  18. Kengo Nakamura, Masaaki Nishino, Norihito Yasuda, Shin-ichi Minato, CompDP: A Framework for Simultaneous Subgraph Counting under Connectivity Constraints, 21st Symposium on Experimental Algorithms (SEA 2023), 2023.
  19. Kengo Nakamura, Takeru Inoue, Masaaki Nishino, Norihito Yasuda, Shin-ichi Minato, Exact and Efficient Network Reliability Evaluation per Outage Scale, IEEE International Conference on Communication, (ICC 2023), 2023.
  20. Kengo Nakamura, Takeru Inoue, Masaaki Nishino, Norihito Yasuda, Shin-ichi Minato, A Fast and Exact Evaluation Algorithm for the Expected Number of Connected Nodes: an Enhanced Network Reliability Measure, IEEE International Conference on Computer Communications (INFOCOM 2023), 2023.
  21. Ryuichi Kanoh, Mahito Sugiyama, Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel, 11th International Conference on Learning Representations (ICLR 2023), 2023.

招待講演

  1. 加納　龍一, Neural Tangent Kernelを用いたアンサンブル学習における木構造の分析, 第22回情報科学技術フォーラム (FIT) トップコンファレンスセッション, 2023.
  2. 杉山 麿人, 機械学習におけるパラメータ空間の再考, 創発的研究支援事業　融合の場2023 深層学習がもたらすブレイクスルーと可能性, 2023.
  3. ガラムカリ 和, 統計多様体上での安定なテンソル・行列分解, 人工知能学会 第125回人工知能基本問題研究会 (SIG-FPAI), 2023.
  4. 井上 克巳, 生成AIと記号推論, 人工知能学会 第125回人工知能基本問題研究会 (SIG-FPAI), 2023.
  5. 西野 正彬, 検証器つき機械学習モデル, 人工知能学会 第125回人工知能基本問題研究会 (SIG-FPAI), 2023.
  6. Kazu Ghalamkari, Tensor Factorization Using Interaction Modeling, Seventh International Workshop on Symbolic-Neural Learning (SNL2023), 2023.

発表

  1. 山田 正嗣, 杉山 麿人, グラフの識別や回帰におけるメッセージパッシングが与える影響の解析, 人工知能学会 第125回人工知能基本問題研究会 (SIG-FPAI), 2023.
  2. 磯邊 猛, 井上 克巳, 強化学習を用いた帰納論理プログラミングにおける探索戦略の学習, 人工知能学会 第125回人工知能基本問題研究会 (SIG-FPAI), 2023.
  3. 森山 総太, 渡邉 晃司, 井上 克巳, グラフニューラルネットワークに基づく極小充足不能部分集合の抽出, 人工知能学会 第125回人工知能基本問題研究会 (SIG-FPAI), 2023.
  4. 伝住 周平, 西野 正彬, 安田 宜仁, 決定グラフ上での最適なk-集合選択問題を高速に解くアルゴリズム, 第37回人工知能学会全国大会（JSAI2023）, 2023.
  5. 中村 健吾, 西野 正彬, 安田 宜仁, 湊 真一, CompDP: 複数の連結性制約の下の部分グラフ数え上げを同時に行う動的計画法, 第37回人工知能学会全国大会（JSAI2023）, 2023.
  6. 小髙充弘, マニャン モルガン, 井上克巳, 多変量時系列からの因果ネットワーク発見による微分方程式系の学習, 第37回人工知能学会全国大会（JSAI2023）, 2023.
  7. 加納龍一, 杉山麿人, 任意の二分木構造に対するTree Neural Tangent Kernel, 第37回人工知能学会全国大会（JSAI2023）, 2023.
  8. ガラムカリ和, 杉山麿人, 非負テンソルの多体モデリング, 第37回人工知能学会全国大会（JSAI2023）, 2023.

受賞

  1. The 1st and 3rd Prizes at ROAD-R Challenge for NeurIPS 2023, Sota Moriyama, Koji Watanabe, Katsumi Inoue, Akihiro Takemura
  2. 人工知能学会 全国大会優秀賞, ガラムカリ和, 杉山麿人 「非負テンソルの多体モデリング」
  3. 人工知能学会 全国大会優秀賞, 加納龍一, 杉山麿人 「任意の二分木構造に対するTree Neural Tangent Kernel」
  4. 人工知能学会 全国大会優秀賞, 中村　健吾，西野　正彬，湊　真一 「CompDP: 複数の連結性制約の下の部分グラフ数え上げを同時に行う動的計画法」

2022年度

論文

  1. Masaya Hagai, Mahito Sugiyama, Koji Tsuda, Takeshi Yanai, Artificial neural network encoding of molecular wavefunctions for quantum computing, Digital Discovery, 2023.
  2. Mitsuhiro Odaka, Morgan Magnin, Katsumi Inoue, Data-Driven and Knowledge-Based Causal Network Discovery for Identifying Differential Equations, AAAI Spring Symposium on Computational Approaches to Scientific Discovery, 2023.
  3. Kazu Ghalamkari, Mahito Sugiyama, Non-negative Low-rank Approximations for Multi-dimensional Arrays on Statistical Manifold, Information Geometry, 2023.
  4. Nicolas Schwind, Katsumi Inoue, Pierre Marquis, Editing Boolean Classifiers: A Belief Change Perspective, 37th AAAI Conference on Artificial Intelligence (AAAI 2023), 2023.
  5. Tuan Nguyen Quoc, Katsumi Inoue, On Converting Logic Programs into Matrices, The 12th International Conference on Agents and Artificial Intelligence (ICAART 2023), 2023.
  6. Tuan Nguyen, Katsumi Inoue, Chiaki Sakama, Linear Algebraic Abduction with Partial Evaluation, The 25th International Symposium on Practical Aspects of Declarative Languages (PADL 2023), 2023.
  7. Masaaki Nishino, Kengo Nakamura, Norihito Yasuda, Generalization Analysis on Learning with a Concurrent Verifier, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
  8. Ryosuke Kojima, Yuji Okamoto, Learning Deep Input-Output Stable Dynamics, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
  9. Hikaru Tomonari, Masaaki Nishino, Akihiro Yamamoto, Robustness Evaluation of Text Classification Models Using Mathematical Optimization and Its Application to Adversarial Training, The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (AACL/IJCNLP 2022 (Findings)), 2022.
  10. Camilo Sarmiento, Gauvain Bourgne, Katsumi Inoue, Jean-Gabriel Ganascia, Action Languages Based Actual Causality in Decision Making Contexts, Proceedings of the 24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2022), 2022.
  11. Koji Watanabe, Katsumi Inoue, Learning State Transition Rules from Hidden Layers of Restricted Boltzmann Machines, Proceedings of Principle and practice of data and Knowledge Acquisition Workshop 2022 (PKAW 2022), 2022.

発表

  1. 加納龍一, 杉山麿人, 任意の二分木構造に対するTree Neural Tangent Kernel, 第25回情報論的学習理論ワークショップ（IBIS2022）, 2022.
  2. ガラムカリ和, 杉山麿人, テンソルの部分二体相互作用近似, 第25回情報論的学習理論ワークショップ（IBIS2022）, 2022.
  3. 西野正彬, 中村健吾, 安田宜仁, 検証器つきモデルの汎化性能解析, 第25回情報論的学習理論ワークショップ（IBIS2022）, 2022.

書籍

  1. Tuan Nguyen Quoc, Katsumi Inoue, Chiaki Sakama, Abductive Logic Programming and Linear Algebraic Computation, Abductive Logic Programming and Linear Algebraic Computation, in: Lorenzo Magnani (ed.), Handbook of Abductive Cognition, 2023.
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