DTM-Based Filtrations. - Hirokazu Anai; Frédéric Chazal; Marc Glisse; Yuichi Ike; Hiroya Inakoshi; Raphaël Tinarrage; Yuhei Umeda (SoCG, 2019)

A General Neural Network Architecture for Persistence Diagrams and Graph Classification. - Mathieu Carrière; Frédéric Chazal; Yuichi Ike; Théo Lacombe; Martin Royer; Yuhei Umeda (CoRR, 2019)

ATOL: Automatic Topologically-Oriented Learning. - Martin Royer; Frédéric Chazal; Yuichi Ike; Yuhei Umeda (CoRR, 2019)

PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures. - Mathieu Carrière; Frédéric Chazal; Yuichi Ike; Théo Lacombe; Martin Royer; Yuhei Umeda (AISTATS, 2020)

A note on stochastic subgradient descent for persistence-based functionals: convergence and practical aspects. - Mathieu Carrière; Frédéric Chazal; Marc Glisse; Yuichi Ike; Hariprasad Kannan (CoRR, 2020)

Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization. - Kentaro Kanamori; Takuya Takagi; Ken Kobayashi; Yuichi Ike; Kento Uemura; Hiroki Arimura (AAAI, 2021)

ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning. - Martin Royer; Frédéric Chazal; Clément Levrard; Yuhei Umeda; Yuichi Ike (AISTATS, 2021)

Optimizing persistent homology based functions. - Mathieu Carrière; Frédéric Chazal; Marc Glisse; Yuichi Ike; Hariprasad Kannan; Yuhei Umeda (ICML, 2021)

Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs. - Théo Lacombe; Yuichi Ike; Mathieu Carrière; Frédéric Chazal; Marc Glisse; Yuhei Umeda (IJCAI, 2021)

Viewpoint Planning of Projector Placement for Spatial Augmented Reality using Star-Kernel Decomposition. - Takefumi Hiraki; Tomohiro Hayase; Yuichi Ike; Takashi Tsuboi; Michio Yoshiwaki (VR Workshops, 2021)

Curvature of point clouds through principal component analysis. - Yasuhiko Asao; Yuichi Ike (CoRR, 2021)

Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees. - Kentaro Kanamori; Takuya Takagi; Ken Kobayashi; Yuichi Ike (AISTATS, 2022)

Change Detection with Probabilistic Models on Persistence Diagrams. - Kohei Ueda; Yuichi Ike; Kenji Yamanishi (ICDM, 2022)

Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length. - Ryo Yuki; Yuichi Ike; Kenji Yamanishi (ICDM, 2022)

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds. - Thibault de Surrel; Felix Hensel; Mathieu Carrière; Théo Lacombe; Yuichi Ike; Hiroaki Kurihara; Marc Glisse; Frédéric Chazal (TAG-ML, 2022)

Vanishing Component Analysis with Contrastive Normalization. - Ryosuke Masuya; Yuichi Ike; Hiroshi Kera (CoRR, 2022)

Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. - Naoki Nishikawa; Yuichi Ike; Kenji Yamanishi (NeurIPS, 2023)

Counterfactual Explanation with Missing Values. - Kentaro Kanamori; Takuya Takagi; Ken Kobayashi; Yuichi Ike (CoRR, 2023)

MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks. - Felix Hensel; Charles Arnal; Mathieu Carrière; Théo Lacombe; Hiroaki Kurihara; Yuichi Ike; Frédéric Chazal (CoRR, 2023)

Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length. - Ryo Yuki; Yuichi Ike; Kenji Yamanishi (Knowl. Inf. Syst., 2023)

Learning Decision Trees and Forests with Algorithmic Recourse. - Kentaro Kanamori; Takuya Takagi; Ken Kobayashi; Yuichi Ike (ICML, 2024)

Distribution-Aligned Sequential Counterfactual Explanation with Local Outlier Factor. - Shoki Yamao; Ken Kobayashi; Kentaro Kanamori; Takuya Takagi; Yuichi Ike; Kazuhide Nakata (PRICAI (1), 2024)

MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Neural Networks. - Charles Arnal; Felix Hensel; Mathieu Carrière; Théo Lacombe; Hiroaki Kurihara; Yuichi Ike; Frédéric Chazal (Trans. Mach. Learn. Res., 2024)