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Yuichi Ike
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2020 – today
- 2024
- [j2]Charles Arnal, Felix Hensel, Mathieu Carrière, Théo Lacombe, Hiroaki Kurihara, Yuichi Ike, Frédéric Chazal:
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Neural Networks. Trans. Mach. Learn. Res. 2024 (2024) - [c13]Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
Learning Decision Trees and Forests with Algorithmic Recourse. ICML 2024 - [i13]Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
Learning Decision Trees and Forests with Algorithmic Recourse. CoRR abs/2406.01098 (2024) - 2023
- [j1]Ryo Yuki, Yuichi Ike, Kenji Yamanishi:
Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length. Knowl. Inf. Syst. 65(12): 5601-5634 (2023) - [c12]Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi:
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. NeurIPS 2023 - [i12]Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
Counterfactual Explanation with Missing Values. CoRR abs/2304.14606 (2023) - [i11]Felix Hensel, Charles Arnal, Mathieu Carrière, Théo Lacombe, Hiroaki Kurihara, Yuichi Ike, Frédéric Chazal:
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks. CoRR abs/2305.13271 (2023) - [i10]Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi:
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. CoRR abs/2307.09259 (2023) - 2022
- [c11]Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees. AISTATS 2022: 1846-1870 - [c10]Ryo Yuki, Yuichi Ike, Kenji Yamanishi:
Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length. ICDM 2022: 666-675 - [c9]Kohei Ueda, Yuichi Ike, Kenji Yamanishi:
Change Detection with Probabilistic Models on Persistence Diagrams. ICDM 2022: 1191-1196 - [c8]Thibault de Surrel, Felix Hensel, Mathieu Carrière, Théo Lacombe, Yuichi Ike, Hiroaki Kurihara, Marc Glisse, Frédéric Chazal:
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds. TAG-ML 2022: 96-106 - [i9]Thibault de Surrel, Felix Hensel, Mathieu Carrière, Théo Lacombe, Yuichi Ike, Hiroaki Kurihara, Marc Glisse, Frédéric Chazal:
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds. CoRR abs/2202.01725 (2022) - [i8]Ryosuke Masuya, Yuichi Ike, Hiroshi Kera:
Vanishing Component Analysis with Contrastive Normalization. CoRR abs/2210.16171 (2022) - 2021
- [c7]Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura:
Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization. AAAI 2021: 11564-11574 - [c6]Martin Royer, Frédéric Chazal, Clément Levrard, Yuhei Umeda, Yuichi Ike:
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning. AISTATS 2021: 1000-1008 - [c5]Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda:
Optimizing persistent homology based functions. ICML 2021: 1294-1303 - [c4]Théo Lacombe, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuhei Umeda:
Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs. IJCAI 2021: 2666-2672 - [c3]Takefumi Hiraki, Tomohiro Hayase, Yuichi Ike, Takashi Tsuboi, Michio Yoshiwaki:
Viewpoint Planning of Projector Placement for Spatial Augmented Reality using Star-Kernel Decomposition. VR Workshops 2021: 583-584 - [i7]Théo Lacombe, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuhei Umeda:
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs. CoRR abs/2105.04404 (2021) - [i6]Yasuhiko Asao, Yuichi Ike:
Curvature of point clouds through principal component analysis. CoRR abs/2106.09972 (2021) - 2020
- [c2]Mathieu Carrière, Frédéric Chazal, Yuichi Ike, Théo Lacombe, Martin Royer, Yuhei Umeda:
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures. AISTATS 2020: 2786-2796 - [i5]Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan:
A note on stochastic subgradient descent for persistence-based functionals: convergence and practical aspects. CoRR abs/2010.08356 (2020) - [i4]Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura:
Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization. CoRR abs/2012.11782 (2020)
2010 – 2019
- 2019
- [c1]Hirokazu Anai, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hiroya Inakoshi, Raphaël Tinarrage, Yuhei Umeda:
DTM-Based Filtrations. SoCG 2019: 58:1-58:15 - [i3]Mathieu Carrière, Frédéric Chazal, Yuichi Ike, Théo Lacombe, Martin Royer, Yuhei Umeda:
A General Neural Network Architecture for Persistence Diagrams and Graph Classification. CoRR abs/1904.09378 (2019) - [i2]Martin Royer, Frédéric Chazal, Yuichi Ike, Yuhei Umeda:
ATOL: Automatic Topologically-Oriented Learning. CoRR abs/1909.13472 (2019) - 2018
- [i1]Hirokazu Anai, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hiroya Inakoshi, Raphaël Tinarrage, Yuhei Umeda:
DTM-based filtrations. CoRR abs/1811.04757 (2018)
Coauthor Index
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last updated on 2024-09-04 01:20 CEST by the dblp team
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