24 citations · 72 across the 12 of their papers we have counts for
12 papers
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs
Raif Rustamov, Subhabrata Majumdar
Collections of probability distributions arise in a variety of applications ranging from user activity pattern analysis to brain connectomics. In practice these distributions can b…
Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns
Raif M. Rustamov, James T. Klosowski
This paper introduces an approach for detecting differences in the first-order structures of spatial point patterns. The proposed approach leverages the kernel mean embedding in a…
Closed-form Expressions for Maximum Mean Discrepancy with Applications to Wasserstein Auto-Encoders
Raif M. Rustamov
The Maximum Mean Discrepancy (MMD) has found numerous applications in statistics and machine learning, most recently as a penalty in the Wasserstein Auto-Encoder (WAE). In this pap…
Graph Matching with Anchor Nodes: A Learning Approach
Nan Hu, Raif M. Rustamov, Leonidas Guibas
In this paper, we consider the weighted graph matching problem with partially disclosed correspondences between a number of anchor nodes. Our construction exploits recently introdu…
Interpretable Graph-Based Semi-Supervised Learning via Flows
Raif M. Rustamov, James T. Klosowski
In this paper, we consider the interpretability of the foundational Laplacian-based semi-supervised learning approaches on graphs. We introduce a novel flow-based learning framewor…
Continuous-Flow Graph Transportation Distances
Justin Solomon, Raif Rustamov, Leonidas Guibas +1
Optimal transportation distances are valuable for comparing and analyzing probability distributions, but larger-scale computational techniques for the theoretically favorable quadr…