Lin, Ya-Wei Eileen

6 publications

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

NeurIPS 2025 Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon

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

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

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

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