1 citations · 1 across the 1 of their papers we have counts for
5 papers
Smoothing graph signals via random spanning forests
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé +1
Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo…
Determinantal Point Processes for Coresets
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard
When faced with a data set too large to be processed all at once, an obvious solution is to retain only part of it. In practice this takes a wide variety of different forms, and am…
Optimized Algorithms to Sample Determinantal Point Processes
Nicolas Tremblay, Simon Barthelme, Pierre-Olivier Amblard
In this technical report, we discuss several sampling algorithms for Determinantal Point Processes (DPP). DPPs have recently gained a broad interest in the machine learning and sta…
Graph sampling with determinantal processes
Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelmé
We present a new random sampling strategy for k-bandlimited signals defined on graphs, based on determinantal point processes (DPP). For small graphs, ie, in cases where the spectr…
Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods"
Simon Barthelme, Magali Beffy, Nicolas Chopin +5
This is a collection of discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead, to appear in the Journal of the Royal Statistical…