output
20132022
most citedPore-geometry recognition: on the importance of quantifying similarity in nanoporous materials

194 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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7 papers · 1 filter

math.ST2021

Estimation and Quantization of Expected Persistence Diagrams

Vincent Divol, Théo Lacombe

Persistence diagrams (PDs) are the most common descriptors used to encode the topology of structured data appearing in challenging learning tasks; think e.g. of graphs, time series…

math.ST2020★ 6 cited

Optimal quantization of the mean measure and applications to statistical learning

Frédéric Chazal, Clément Levrard, Martin Royer

This paper addresses the case where data come as point sets, or more generally as discrete measures. Our motivation is twofold: first we intend to approximate with a compactly supp…

math.ST2019★ 11 cited

Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems

Abhishake Rastogi, Gilles Blanchard, Peter Mathé

We study a non-linear statistical inverse learning problem, where we observe the noisy image of a quantity through a non-linear operator at some random design points. We consider t…

math.ST2018★ 1 cited

Robust Bregman Clustering

Aurélie Fischer, Clément Levrard, Claire Brécheteau

Using a trimming approach, we investigate a k-means type method based on Bregman divergences for clustering data possibly corrupted with clutter noise. The main interest of Bregman…

math.ST2018★ 13 cited

Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension

Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo +1

We derive concentration inequalities for the supremum norm of the difference between a kernel density estimator (KDE) and its point-wise expectation that hold uniformly over the se…

math.ST2018★ 4 cited

Post hoc false positive control for spatially structured hypotheses

Guillermo Durand, Gilles Blanchard, Pierre Neuvial +1

In a high dimensional multiple testing framework, we present new confidence bounds on the false positives contained in subsets S of selected null hypotheses. The coverage probabili…

math.ST2017★ 49 cited

Post hoc inference via joint family-wise error rate control

Gilles Blanchard, Pierre Neuvial, Etienne Roquain

We introduce a general methodology for post hoc inference in a large-scale multiple testing framework. The approach is called "user-agnostic" in the sense that the statistical guar…