1. |
Tomohiro Asano, Stéphane Guillermou, Vincent Humilière, Yuichi Ike, Claude Viterbo, The γ-support as a micro-support, *Comptes Rendus Mathématique *, 2023.12. |

2. |
Kohei Ueda, Yuichi Ike, and Kenji Yamanishi
, Change Detection with Probabilistic Models on Persistence Diagrams, *2022 IEEE International Conference on Data Mining (ICDM)*, 10.1109/ICDM54844.2022.00153, 2022.12. |

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)*, 10.1109/ICDM54844.2022.00077, 2022.12. |

4. |
Tomohiro Asano, Yuichi Ike, Sheaf quantization and intersection of rational Lagrangian immersions, *Annales de l'Institut Fourier*, 10.5802/aif.3554, 1-55, 2022.11. |

5. |
Yasuaki Hiraoka, Yuichi Ike, Michio Yoshiwaki, Algebraic stability theorem for derived categories of zigzag persistence modules, *Journal of Topology and Analysis*, 2022.08, We study distances on zigzag persistence modules from the viewpoint of derived categories. It is known that the derived categories of ordinary and arbitrary zigzag persistence modules are equivalent. Through this derived equivalence, we define distances on the derived category of arbitrary zigzag persistence modules and prove an algebraic stability theorem. We also compare our distance with the distance for purely zigzag persistence modules introduced by Botnan--Lesnick and the sheaf-theoretic convolution distance due to Kashiwara--Schapira.. |

6. |
Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Optimizing persistent homology based functions, *Proceedings of the 38th International Conference on Machine Learning (ICML2021)*, 2021.07, Solving optimization tasks based on functions and losses with a topological flavor is a very active, growing field of research in data science and Topological Data Analysis, with applications in non-convex optimization, statistics and machine learning. However, the approaches proposed in the literature are usually anchored to a specific application and/or topological construction, and do not come with theoretical guarantees. To address this issue, we study the differentiability of a general map associated with the most common topological construction, that is, the persistence map. Building on real analytic geometry arguments, we propose a general framework that allows us to define and compute gradients for persistence-based functions in a very simple way. We also provide a simple, explicit and sufficient condition for convergence of stochastic subgradient methods for such functions. This result encompasses all the constructions and applications of topological optimization in the literature. Finally, we provide associated code, that is easy to handle and to mix with other non-topological methods and constraints, as well as some experiments showcasing the versatility of our approach.. |

7. |
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs. |

8. |
ATOL: Measure Vectorisation for Automatic Topologically-Oriented Learning Robust topological information commonly comes in the form of a set of persistence diagrams, finite measures that are in nature uneasy to affix to generic machine learning frameworks. We introduce a learnt, unsupervised measure vectorisation method and use it for reflecting underlying changes in topological behaviour in machine learning contexts. Relying on optimal measure quantisation results the method is tailored to efficiently discriminate important plane regions where meaningful differences arise. We showcase the strength and robustness of our approach on a number of applications, from emulous and modern graph collections where the method reaches state-of-the-art performance to a geometric synthetic dynamical orbits problem. The proposed methodology comes with only high level tuning parameters such as the total measure encoding budget, and we provide a completely open access software.. |

9. |
Viewpoint Planning of Projector Placement for Spatial Augmented Reality using Star-Kernel Decomposition. |

10. |
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures. |

11. |
Tomohiro Asano and Yuichi Ike, Persistence-like distance on Tamarkin's category and symplectic displacement energy, *Journal of Symplectic Geometry*, https://dx.doi.org/10.4310/JSG.2020.v18.n3.a1, 18, 3, 613-649, 2020.06. |

12. |
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura, Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization, *Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI2021)*, 2021.02, Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbation vector, a user can interpret it as an "action" for obtaining one's desired decision result. In practice, however, showing only a perturbation vector is often insufficient for users to execute the action. The reason is that if there is an asymmetric interaction among features, such as causality, the total cost of the action is expected to depend on the order of changing features. Therefore, practical CE methods are required to provide an appropriate order of changing features in addition to a perturbation vector. For this purpose, we propose a new framework called Ordered Counterfactual Explanation (OrdCE). We introduce a new objective function that evaluates a pair of an action and an order based on feature interaction. To extract an optimal pair, we propose a mixed-integer linear optimization approach with our objective function. Numerical experiments on real datasets demonstrated the effectiveness of our OrdCE in comparison with unordered CE methods.. |

13. |
DTM-based filtrations. |

14. |
Yuichi Ike, Compact exact Lagrangian intersections in cotangent bundles via sheaf quantization, *Publications of the Research Institute for Mathematical Sciences*, 55, 4, 737-778, 2019.10. |

15. |
Categorical localization for the coherent-constructible correspondence. |

16. |
Hyperbolic localization and Lefschetz fixed point formulas for higher-dimensional fixed point sets. |

17. |
Yuichi Ike, Microlocal Lefschetz classes of graph trace kernels, 10.4171/PRIMS/175, 52, 1, 83-101, 2016.01. |