Yuichi Ike

According to our database, Yuichi Ike authored at least 22 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

Learning Decision Trees and Forests with Algorithmic Recourse.

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length.

Ryo Yuki, Yuichi Ike, Kenji Yamanishi

Knowl. Inf. Syst., December, 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

Counterfactual Explanation with Missing Values.

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike

CoRR, 2023

Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds.

Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Vanishing Component Analysis with Contrastive Normalization.

Ryosuke Masuya, Yuichi Ike, Hiroshi Kera

CoRR, 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

Proceedings of the Topological, 2022

Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length.

Ryo Yuki, Yuichi Ike, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2022

Change Detection with Probabilistic Models on Persistence Diagrams.

Kohei Ueda, Yuichi Ike, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2022

Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees.

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Curvature of point clouds through principal component analysis.

Yasuhiko Asao, Yuichi Ike

CoRR, 2021

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

Takefumi Hiraki, Tomohiro Hayase, Yuichi Ike, Takashi Tsuboi, Michio Yoshiwaki

Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2021

Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs.

Théo Lacombe, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuhei Umeda

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Optimizing persistent homology based functions.

Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda

Proceedings of the 38th International Conference on Machine Learning, 2021

ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning.

Martin Royer, Frédéric Chazal, Clément Levrard, Yuhei Umeda, Yuichi Ike

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization.

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

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

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

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

ATOL: Automatic Topologically-Oriented Learning.

Martin Royer, Frédéric Chazal, Yuichi Ike, Yuhei Umeda

CoRR, 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

DTM-Based Filtrations.

Hirokazu Anai, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hiroya Inakoshi, Raphaël Tinarrage, Yuhei Umeda

Proceedings of the 35th International Symposium on Computational Geometry, 2019