5 citations · 7 across the 11 of their papers we have counts for
11 papers
Low-Discrepancy Set Post-Processing via Gradient Descent
François Clément, Linhang Huang, Woorim Lee +3
The construction of low-discrepancy sets, used for uniform sampling and numerical integration, has recently seen great improvements based on optimization and machine learning techn…
Optimizing Kernel Discrepancies via Subset Selection
Deyao Chen, François Clément, Carola Doerr +1
Kernel discrepancies are a powerful tool for analyzing worst-case errors in quasi-Monte Carlo (QMC) methods. Building on recent advances in optimizing such discrepancy measures, we…
Obtaining the Chamanara Surface from the van der Corput sequence
Zawad Chowdhury, Francois Clement, Max Horwitz
We investigate a family of -regular graphs constructed to test for the presence of combinatorial structure in a sequence of distinct real numbers. We show that the graphs constr…
Robust Online Sampling from Possibly Moving Target Distributions
François Clément, Stefan Steinerberger
We suppose we are given a list of points , a target probability measure and are asked to add additional points so that…
On the largest singular vector of the Redheffer matrix
François Clément, Stefan Steinerberger
The Redheffer matrix is defined by setting if or divides and 0 otherwise. One of its many interesting properties is tha…
Transforming the Challenge of Constructing Low-Discrepancy Point Sets into a Permutation Selection Problem
François Clément, Carola Doerr, Kathrin Klamroth +1
Low discrepancy point sets have been widely used as a tool to approximate continuous objects by discrete ones in numerical processes, for example in numerical integration. Followin…
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms
François Clément, Diederick Vermetten, Jacob de Nobel +3
The star discrepancy is a measure for the regularity of a finite set of points taken from . Low discrepancy point sets are highly relevant for Quasi-Monte Car…
Heuristic Approaches to Obtain Low-Discrepancy Point Sets via Subset Selection
François Clément, Carola Doerr, Luís Paquete
Building upon the exact methods presented in our earlier work [J. Complexity, 2022], we introduce a heuristic approach for the star discrepancy subset selection problem. The heuris…
Partitions for stratified sampling
Francois Clement, Nathan Kirk, Florian Pausinger
Classical jittered sampling partitions into cubes for a positive integer and randomly places a point inside each of them, providing a point set of size …
A Coq Formalization of Lebesgue Integration of Nonnegative Functions
Sylvie Boldo, François Clément, Florian Faissole +2
Integration, just as much as differentiation, is a fundamental calculus tool that is widely used in many scientific domains. Formalizing the mathematical concept of integration and…
Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions
François Clèment, Carola Doerr, Luís Paquete
Motivated by applications in instance selection, we introduce the star discrepancy subset selection problem, which consists of finding a subset of m out of n points that minimizes…