1 citations · 1 across the 1 of their papers we have counts for
6 papers
Finite sample bounds for barycenter estimation in geodesic spaces
Victor-Emmanuel Brunel, Jordan Serres
We study the problem of estimating the barycenter of a distribution given i.i.d. data in a geodesic space. Assuming an upper curvature bound in Alexandrov's sense and a support con…
Functional Central Limit Theorem for Stochastic Gradient Descent
Kessang Flamand, Victor-Emmanuel Brunel
We study the asymptotic shape of the trajectory of the stochastic gradient descent algorithm applied to a convex objective function. Under mild regularity assumptions, we prove a f…
Bernstein-von Mises theorem for log-concave posteriors
Victor-Emmanuel Brunel
We prove new, general versions of Bernstein-von Mises theorem for both well-specified and misspecified models when the log-likelihood is concave in the parameter and the prior dist…
On the continuity of geodesically convex functions on Riemannian manifolds
Victor-Emmanuel Brunel, Pierre Pansu
In this short note, we prove that all geodesically convex functions defined on a Riemannian manifold are continuous in the interior of their domain. This is a folklore result, but…
Asymptotics of constrained -estimation under convexity
Victor-Emmanuel Brunel
M-estimation, aka empirical risk minimization, is at the heart of statistics and machine learning: Classification, regression, location estimation, etc. Asymptotic theory is well u…
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Imad Aouali, Victor-Emmanuel Brunel, David Rohde +1
In interactive systems, actions are often correlated, presenting an opportunity for more sample-efficient off-policy evaluation (OPE) and learning (OPL) in large action spaces. We…