14 citations · 58 across the 16 of their papers we have counts for
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Kernel Stein Discrepancy on Lie Groups: Theory and Applications
Xiaoda Qu, Xiran Fan, Baba C. Vemuri
Distributional approximation is a fundamental problem in machine learning with numerous applications across all fields of science and engineering and beyond. The key challenge in m…
A Framework for Improving the Characterization Scope of Stein's Method on Riemannian Manifolds
Xiaoda Qu, Baba C. Vemuri
Stein's method has been widely used to achieve distributional approximations for probability distributions defined in Euclidean spaces. Recently, techniques to extend Stein's metho…
Shrinkage Estimation of the Frechet Mean in Lie groups
Chun-Hao Yang, Baba C. Vemuri
Data in non-Euclidean spaces are commonly encountered in many fields of Science and Engineering. For instance, in Robotics, attitude sensors capture orientation which is an element…
An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices
Chun-Hao Yang, Hani Doss, Baba C. Vemuri
The James-Stein estimator is an estimator of the multivariate normal mean and dominates the maximum likelihood estimator (MLE) under squared error loss. The original work inspired…
Gaussian distributions on Riemannian symmetric spaces: statistical learning with structured covariance matrices
Salem Said, Hatem Hajri, Lionel Bombrun +1
The Riemannian geometry of covariance matrices has been essential to several successful applications, in computer vision, biomedical signal and image processing, and radar data pro…