35 citations · 37 across the 11 of their papers we have counts for
7 papers · 1 filter
Complex Interpolation of Matrices with an application to Multi-Manifold Learning
Adi Arbel, Stefan Steinerberger, Ronen Talmon
Given two symmetric positive-definite matrices , we study the spectral properties of the interpolation for . The pr…
It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph
Harel Mendelman, Haggai Maron, Ronen Talmon
Graph Neural Networks (GNNs) excel at analyzing graph-structured data but struggle on heterophilic graphs, where connected nodes often belong to different classes. While this chall…
Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
High-dimensional data often exhibit hierarchical structures in both modes: samples and features. Yet, most existing approaches for hierarchical representation learning consider onl…
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimension…
Joint Geometric and Topological Analysis of Hierarchical Datasets
Lior Aloni, Omer Bobrowski, Ronen Talmon
In a world abundant with diverse data arising from complex acquisition techniques, there is a growing need for new data analysis methods. In this paper we focus on high-dimensional…
Option Discovery in the Absence of Rewards with Manifold Analysis
Amitay Bar, Ronen Talmon, Ron Meir
Options have been shown to be an effective tool in reinforcement learning, facilitating improved exploration and learning. In this paper, we present an approach based on spectral g…