19 citations · 26 across the 11 of their papers we have counts for
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math.ST2023
Covariance alignment: from maximum likelihood estimation to Gromov-Wasserstein
Yanjun Han, Philippe Rigollet, George Stepaniants
Feature alignment methods are used in many scientific disciplines for data pooling, annotation, and comparison. As an instance of a permutation learning problem, feature alignment…
math.ST2021★ 2 cited
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces
George Stepaniants
We propose a new data-driven approach for learning the fundamental solutions (Green's functions) of various linear partial differential equations (PDEs) given sample pairs of input…
math.ST2020★ 4 cited
Fast and Smooth Interpolation on Wasserstein Space
Sinho Chewi, Julien Clancy, Thibaut Le Gouic +3
We propose a new method for smoothly interpolating probability measures using the geometry of optimal transport. To that end, we reduce this problem to the classical Euclidean sett…