Hyperbolic Procrustes Analysis Using Riemannian Geometry. - Ya-Wei Eileen Lin; Yuval Kluger; Ronen Talmon (NeurIPS, 2021)

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry. - Ya-Wei Eileen Lin; Tal Shnitzer; Ronen Talmon; Franz Villarroel-Espindola; Shruti Desai; Kurt A. Schalper; Yuval Kluger (PLoS Comput. Biol., 2021)

Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning. - Ya-Wei Eileen Lin; Ronald R. Coifman; Gal Mishne; Ronen Talmon (ICML, 2023)

Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets. - Ya-Wei Eileen Lin; Yuval Kluger; Ronen Talmon (ICASSP, 2024)

Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters. - Ya-Wei Eileen Lin; Ronen Talmon; Ron Levie (NeurIPS, 2024)

Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding. - Thomas Dagès; Simon Weber 002; Ya-Wei Eileen Lin; Ronen Talmon; Daniel Cremers; Michael Lindenbaum; Alfred M. Bruckstein; Ron Kimmel (CVPR, 2025)

Hyperbolic Distance Based on EMD and Diffusion for Hyperspectral Imaging. - Elad Lavi; Amir Bourvine; Ya-Wei Eileen Lin; Ronen Talmon (ICASSP, 2025)

Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy. - Ya-Wei Eileen Lin; Ronald R. Coifman; Gal Mishne; Ronen Talmon (ICLR, 2025)

Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance. - Ya-Wei Eileen Lin; Ronald R. Coifman; Gal Mishne; Ronen Talmon (CoRR, 2025)