2 citations · 4 across the 12 of their papers we have counts for
18 papers
A uniform relative deviation inequality for VC-subgraph classes
François Portier
We establish a new Bernstein-type deviation inequality for classes of functions whose complexity is characterized through subgraphs. The inequality is non-asymptotic, involves expl…
Revisiting local regression: shape regularity, uniform rates, and the limits of random splits
Jérémy Bettinger, François Portier, Adrien Saumard
Considering pointwise and sup-norm estimation, we analyze the non-asymptotic behavior of local averaging estimators for Lipschitz regression functions. Building on a general deviat…
Importance Sampling Optimization with Laplace Principle
Radu-Alexandru Dragomir, François Portier, Victor Priser
Grid search and random search are widely used techniques for hyperparameter tuning in machine learning, especially when gradient information is unavailable. In these methods, a fin…
On the pointwise and sup-norm errors for local regression estimators
Jérémy Bettinger, François Portier, Adrien Saumard
In this paper, we analyze the behavior of various non-parametric local regression estimators, i.e. estimators that are based on local averaging, for estimating a Lipschitz regressi…
A theory of shape regularity for local regression maps
Jérémy Bettinger, François Portier, Adrien Saumard
We introduce the concept of shape-regular regression maps as a framework to derive optimal rates of convergence for various non-parametric local regression estimators. Using Vapnik…
Stochastic mirror descent for nonparametric adaptive importance sampling
Pascal Bianchi, Bernard Delyon, Victor Priser +1
This paper addresses the problem of approximating an unknown probability distribution with density -- which can only be evaluated up to an unknown scaling factor -- with the he…