6 citations · 19 across the 9 of their papers we have counts for
11 papers
Gaussian Process Regression in the Flat Limit
Simon Barthelmé, Pierre-Olivier Amblard, Nicolas Tremblay +1
Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regressio…
Variance reduction in stochastic methods for large-scale regularised least-squares problems
Yusuf Pilavcı, Pierre-Olivier Amblard, Simon Barthelmé +1
Large dimensional least-squares and regularised least-squares problems are expensive to solve. There exist many approximate techniques, some deterministic (like conjugate gradient)…
Determinantal Point Processes in the Flat Limit
Simon Barthelmé, Nicolas Tremblay, Konstantin Usevich +1
Determinantal point processes (DPPs) are repulsive point processes where the interaction between points depends on the determinant of a positive-semi definite matrix. In this paper…
Extended L-ensembles: a new representation for Determinantal Point Processes
Nicolas Tremblay, Simon Barthelmé, Konstantin Usevich +1
Determinantal point processes (DPPs) are a class of repulsive point processes, popular for their relative simplicity. They are traditionally defined via their marginal distribution…
Determinantal Point Processes in the Flat Limit: Extended L-ensembles, Partial-Projection DPPs and Universality Classes
Simon Barthelmé, Nicolas Tremblay, Konstantin Usevich +1
Determinantal point processes (DPPs) are repulsive point processes where the interaction between points depends on the determinant of a positive-semi definite matrix. The contribut…
Estimating the inverse trace using random forests on graphs
Simon Barthelmé, Nicolas Tremblay, Alexandre Gaudillière +2
Some data analysis problems require the computation of (regularised) inverse traces, i.e. quantities of the form $\Tr (q \bI + \bL)^{-1}$. For large matrices, direct methods are un…
Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes
Simon Barthelmé, Pierre-Olivier Amblard, Nicolas Tremblay
Determinantal Point Processes (DPPs) are popular models for point processes with repulsion. They appear in numerous contexts, from physics to graph theory, and display appealing th…
Divide and conquer in ABC: Expectation-Progagation algorithms for likelihood-free inference
Simon Barthelmé, Nicolas Chopin, Vincent Cottet
ABC algorithms are notoriously expensive in computing time, as they require simulating many complete artificial datasets from the model. We advocate in this paper a "divide and con…
Expectation Propagation in the large-data limit
Guillaume Dehaene, Simon Barthelmé
Expectation Propagation (Minka, 2001) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typica…
Expectation-Propagation for Likelihood-Free Inference
Simon Barthelmé, Nicolas Chopin
Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistica…
A flexible Bayesian method for adaptive measurement in psychophysics
Simon Barthelmé, Pascal Mamassian
In psychophysical experiments time and the limited goodwill of participants is usually a major constraint. This has been the main motivation behind the early development of adaptiv…