Patrik Robert Gerber — accepted AI-conference papers

2023

Kernel-Based Tests for Likelihood-Free Hypothesis Testing

Patrik Robert Gerber, Tianze Jiang, Yury Polyanskiy, Rui Sun

NeurIPS 2023poster

Given $n$ observations from two balanced classes, consider the task of labeling an additional $m$ inputs that are known to all belong to \emph{one} of the two classes. Special cases of this problem are well-known: with complete knowledge of class distributions ($n=\infty$) the problem is solved opt…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2021

Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent

Jason Altschuler, Sinho Chewi, Patrik Robert Gerber, Austin J Stromme

NeurIPS 2021spotlight

We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric. Although the objective is geodesically non-convex, Riemannian gradient descent empirically converges rapidly, in fact faster than off-the-shelf methods su…

Cited by 60 Source ↗PDF ↗Scholar ↗