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.

collaborating institutions
Showing 2018Show all

7 papers · 1 filter

cs.CG2018★ 5 cited

On Order Types of Random Point Sets

Olivier Devillers, Philippe Duchon, Marc Glisse +1

A simple method to produce a random order type is to take the order type of a random point set. We conjecture that many probability distributions on order types defined in this way…

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…

cs.CG2018★ 12 cited

Strong Collapse for Persistence

Jean-Daniel Boissonnat, Siddharth Pritam, Divyansh Pareek

We introduce a fast and memory efficient approach to compute the persistent homology (PH) of a sequence of simplicial complexes. The basic idea is to simplify the complexes of the…

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…

stat.ML2018★ 31 cited

Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport

Théo Lacombe, Marco Cuturi, Steve Oudot

Persistence diagrams (PDs) are now routinely used to summarize the underlying topology of complex data. Despite several appealing properties, incorporating PDs in learning pipeline…

cs.CG2018★ 22 cited

The density of expected persistence diagrams and its kernel based estimation

Frédéric Chazal, Vincent Divol

Persistence diagrams play a fundamental role in Topological Data Analysis where they are used as topological descriptors of filtrations built on top of data. They consist in discre…