Yuichi Ike

Publications (Refereed)

Microlocal Sheaf Theory and Symplectic Geometry

1. Tomohiro Asano, Stéphane Guillermou, Yuichi Ike, and Claude Viterbo
   Regular Lagrangians are smooth Lagrangians
   J. Math. Soc. Japan (JMSJ)
   DOI: 10.2969/jmsj/93799379, arXiv:2407.00395

2. Yuichi Ike and Tatsuki Kuwagaki
   Tamarkin's separation theorem for non-compact objects in cotangent bundles
   Proc. Amer. Math. Soc. 153(8), 3555--3561, 2025
   DOI: 10.1090/proc/17228, arXiv:2406.08247

3. Tomohiro Asano , Yuichi Ike, and Wenyuan Li
   Lagrangian cobordism and shadow distance in Tamarkin category
   Sel. Math. New Ser. 31, 45, 2025
   DOI: 10.1007/s00029-025-01034-9, arXiv:2312.14429

4. Tomohiro Asano and Yuichi Ike
   Completeness of derived interleaving distances and sheaf quantization of non-smooth objects
   Math. Ann. 390, 2991--3037, 2024
   DOI: 10.1007/s00208-024-02815-x (view-only link),  arXiv:2201.02598

5. Tomohiro Asano, Stéphane Guillermou, Vincent Humilière, Yuichi Ike, and Claude Viterbo
   The γ-support as a micro-support
   Comptes Rendus. Mathématique 361, 1333--1340, 2023
   DOI: 10.5802/crmath.499, arXiv:2211.13945 

6. Tomohiro Asano and Yuichi Ike
   Sheaf quantization and intersection of rational Lagrangian immersions
   Ann. Inst. Fourier 73(4), 1533--1587, 2023
   DOI: 10.5802/aif.3554, arXiv:2005.05088

7. Tomohiro Asano and Yuichi Ike
   Persistence-like distance on Tamarkin's category and symplectic displacement energy
   J. Symp. Geom 18(3), 613--649, 2020
   DOI: 10.4310/JSG.2020.v18.n3.a1, arXiv:1712.06847

8. Yuichi Ike
   Compact exact Lagrangian intersections in cotangent bundles via sheaf quantization
   Publ. RIMS, Kyoto Univ. 55(4) 737--778, 2019
   DOI: 10.4171/PRIMS/55-4-3, arXiv:1701.02057

9. Yuichi Ike and Tatsuki Kuwagaki
   Categorical localization for the coherent-constructible correspondence
   Publ. RIMS, Kyoto Univ. 55(1) 1--24, 2019
   DOI: 10.4171/PRIMS/55-1-1, arXiv:1609.01177 

Topological Data Analysis and Its Applications

1. Naoki Nishikawa, Yuichi Ike, and Kenji Yamanishi
   Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
   Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
   article link, arxiv:2307.09259

2. Thibault de Surrel, Felix Hensel, Mathieu Carrière, Théo Lacombe, Yuichi Ike, Hiroaki Kurihara, Marc Glisse, and Frédéric Chazal
   RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds
   Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, PMLR 196:96-106, 2022.
   article link, arXiv:2202.01725

3. Yasuaki Hiraoka, Yuichi Ike, and Michio Yoshiwaki
   Algebraic stability theorem for derived categories of zigzag persistence modules
   Journal of Topology and Analysis, 2022
   DOI: 10.1142/S1793525322500091,  arXiv:2006.06924

4. Théo Lacombe, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, and Yuhei Umeda
   Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
   Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), 2021
   article link, arXiv:2105.04404

5. Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, and Yuhei Umeda
   Optimizing persistent homology based functions
   Proceedings of the 38th  International Conference on Machine Learning  (ICML 2021), 2021; oral presentation
   article link, arXiv:2010.08356

6. Martin Royer, Frédéric Chazal, Clément Levrard, Yuhei Umeda, and Yuichi Ike
   ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning
   Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), 2021
   article link, arXiv:1909.13472 

7. Mari Kajitani, Ken Kobayashi, Yuichi Ike, Takehiko Yamanashi, Yuhei Umeda, Yoshimasa Kadooka, and Gen Shinozaki
   Application of Topological Data Analysis to Delirium Detection
   Topological Data Analysis and Beyond, Workshop at NeurIPS 2020, 2020
   article link (OpenReview)

8. Mathieu Carrière, Frédéric Chazal, Yuichi Ike, Théo Lacombe, Martin Royer, and Yuhei Umeda
   PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
   Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), 2020
   article link, arXiv:1909.13472 

9. Hirokazu Anai, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hiroya Inakoshi, Raphaël Tinarrage, and Yuhei Umeda
   DTM-based filtrations
   Proceedings of 35th International Symposium on Computational Geometry (SoCG 2019)/Topological Data Analysis. Abel Symposia, vol 15
   DOI: 10.1007/978-3-030-43408-3_2, arXiv:1811.04757

Sheaf Theory and Lefschetz Fixed Point Formula

1. Yuichi Ike, Yutaka Matsui, and Kiyoshi Takeuchi
   Hyperbolic localization and Lefschetz fixed point formulas for higher-dimensional fixed point sets
   Int. Math. Res. Not. 2018(15), 4852--4898, 2017
   DOI: 10.1093/imrn/rnx030, arXiv:1504.04185
   Supplement: Yuichi Ike; Hyperbolic localization via shrinking subbundles arXiv:1602.04651

2. Yuichi Ike
   Microlocal Lefschetz classes of graph trace kernels
   Publ. RIMS, Kyoto Univ. 52(1), 83--101, 2016
   DOI: 10.4171/PRIMS/175, arXiv:1504.05439

Counterfactual explanation

1. Shoki Yamao, Ken Kobayashi, Kentaro. Kanamori, Takuya Takagi, Yuichi Ike, and Kazuhide Nakata
   Distribution-aligned sequential counterfactual explanation with local outlier factor
   Proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence (PRICAI2024)
   DOI: 10.1007/978-981-96-0116-5_20 

2. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, and Yuichi Ike
   Learning Decision Trees and Forests with Algorithmic Recourse
   Proceedings of the 41st International Conference on Machine Learning (ICML2024), 2024; spotlight presentation
   article link, arXiv:2406.01098

3. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, and Yuichi Ike
   Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees
   Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), 2022
   article link

4. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, and Hiroki Arimura
   Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization
   Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI2021), 2021
   DOI: 10.1609/aaai.v35i13.17376, arXiv:2012.11782

Others

1. Felix Hensel, Charles Arnal, Mathieu Carrière, Théo Lacombe, Hiroaki Kurihara, Yuichi Ike, and Frédéric Chazal
   MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks
   Transactions on Machine Learning Research (TMLR), May 2024
   article link (OpenReview), arXiv:2305.13271

2. Ryo Yuki, Yuichi Ike, and Kenji Yamanishi
   Dimensionality selection for hyperbolic embedding using decomposed normalized maximum likelihood code-length
   Knowledge and Information Systems, 2023
   DOI: 10.1007/s10115-023-01934-2 

3. Ryo Yuki, Yuichi Ike, and Kenji Yamanishi
   Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length
   2022 IEEE International Conference on Data Mining (ICDM), 666--675, 2022
   DOI: 10.1109/ICDM54844.2022.00077 

4. Kohei Ueda, Yuichi Ike, and Kenji Yamanishi
   Change Detection with Probabilistic Models on Persistence Diagrams
   2022 IEEE International Conference on Data Mining (ICDM), 1191--1196, 2022
   DOI: 10.1109/ICDM54844.2022.00153 

5. Takefumi Hiraki, Tomohiro Hayase, Yuichi Ike, Takashi Tsuboi, and Michio Yoshiwaki
   Viewpoint Planning of Projector Placement for Spatial Augmented Reality using Star-Kernel Decomposition
   2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2021
   DOI: 10.1109/VRW52623.2021.00174

6. 渡邉陽太, 川畑佑典, 三浦達彦, 池祐一, 江間陽平, 剱持智哉, 松原宰栄, 米田剛, 千葉優作, 柏原崇人
   磁気流体緩和法の初期条件依存性∼磁力線形状とエネルギー∼
   数理科学実践研究レター 2018, LMSR 2018-20, 2018
   DOI: 10.15083/00076820

7. 川畑佑典, 三浦達彦, 渡邉陽太, 池祐一, 江間陽平, 剱持智哉, 松原宰栄, 米田剛, 千葉優作, 柏原崇人
   磁気流体緩和法の初期条件依存性∼force-free alphaの空間分布∼
   数理科学実践研究レター 2018, LMSR 2018-10, 2018
   DOI: 10.15083/00076811

Proceedings, Surveys (Refereed)

1. 池祐一
   位相的データ解析と機械学習への応用
   応用数理学会誌「応用数理」, 31 巻, 3 号, 2022
   DOI: 10.11540/bjsiam.32.3_139

Proceedings, Surveys (Non-refereed)

1. 代数解析学と層：佐藤超函数やD加群との関連，雑誌「数理科学」2025年9月号 No.747 多彩な拡がりをもつ《層》の魅力，pp.30-37，サイエンス社，2025. 記事PDF，正誤表

2. パーシステントホモロジーと機械学習，雑誌「数理科学」2023年6月号 No.720 トポロジカルデータ解析の拡がり

3. Microlocal sheaf theory and energy estimates in symplectic geometry, The 68th geometry symposium

4. 層の圏上のパーシステンス的距離とシンプレクティック幾何における分離エネルギー, The 68th topology symposium

Preprints

1. Tomohiro Asano, Yuichi Ike, Christopher Kuo, and Wenyuan Li
   C^0-rigidity of Legendrians and coisotropics via sheaf quantization
   preprint (2025)
   arXiv:2510.01746

2. Tomohiro Asano and Yuichi Ike
   The rectifiable rectangular peg problem
   preprint (2024)
   arXiv:2412.21057

3. Yuichi Ike and Tatsuki Kuwagaki
   Microlocal categories over Novikov rings
   preprint (2023)
   arXiv:2307.01561

4. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, and Yuichi Ike
   Counterfactual Explanation with Missing Values
   preprint (2023)
   arXiv:2304.14606

Book

1. 池 祐一，E. G. エスカラ，大林一平，鍛冶静雄
   位相的データ解析から構造発見へ：パーシステントホモロジーを中心に
   AI/データサイエンス ライブラリ “基礎から応用へ”，サイエンス社．link

Misc

1. Tomohiro Asano and Yuichi Ike
   A note on Hamiltonian stability for sheaves
   preprint (2023), merged into "Completeness of derived interleaving distances and sheaf quantization of non-smooth objects"
   arXiv:2301.10598

2. Ryosuke Masuya, Yuichi Ike, and Hiroshi Kera
   Vanishing Component Analysis with Contrastive Normalization
   preprint (2022)
   arXiv:2210.16171

3. Yasuhiko Asao and Yuichi Ike
   Curvature of point clouds through principal component analysis
   preprint (2021)
   arXiv:2106.09972