Simon Barthelmé
Simon Barthelmé
CNRS Research Fellow, Gipsa-lab
Verified email at gipsa-lab.fr - Homepage
Title
Cited by
Cited by
Year
Expectation propagation for likelihood-free inference
S Barthelmé, N Chopin
Journal of the American Statistical Association 109 (505), 315-333, 2014
932014
Expectation propagation for likelihood-free inference
S Barthelmé, N Chopin
Journal of the American Statistical Association 109 (505), 315-333, 2014
872014
Spatial statistics and attentional dynamics in scene viewing
R Engbert, HA Trukenbrod, S Barthelmé, FA Wichmann
Journal of vision 15 (1), 14-14, 2015
802015
Flexible mechanisms underlie the evaluation of visual confidence
S Barthelmé, P Mamassian
Proceedings of the National Academy of Sciences 107 (48), 20834-20839, 2010
772010
Evaluation of objective uncertainty in the visual system
S Barthelmé, P Mamassian
PLoS Comput Biol 5 (9), e1000504, 2009
652009
Improved classification images with sparse priors in a smooth basis
PJ Mineault, S Barthelme, CC Pack
Journal of Vision 9 (10), 17-17, 2009
452009
Expectation propagation in the large-data limit
G Dehaene, S Barthelmé
arXiv preprint arXiv:1503.08060, 2015
442015
Modeling fixation locations using spatial point processes
S Barthelmé, H Trukenbrod, R Engbert, F Wichmann
Journal of vision 13 (12), 1-1, 2013
442013
A probabilistic approach to demixing odors
A Grabska-Barwińska, S Barthelmé, J Beck, ZF Mainen, A Pouget, ...
Nature neuroscience 20 (1), 98-106, 2017
332017
Graph sampling with determinantal processes
N Tremblay, PO Amblard, S Barthelmé
2017 25th European Signal Processing Conference (EUSIPCO), 1674-1678, 2017
292017
ABC-EP: expectation propagation for likelihoodfree Bayesian computation
S Barthelmé, N Chopin
ICML, 2011
252011
Bounding errors of expectation-propagation
GP Dehaene, S Barthelmé
arXiv preprint arXiv:1601.02387, 2016
222016
Determinantal Point Processes for Coresets.
N Tremblay, S Barthelmé, PO Amblard
Journal of Machine Learning Research 20 (168), 1-70, 2019
152019
Optimized algorithms to sample determinantal point processes
N Tremblay, S Barthelme, PO Amblard
arXiv preprint arXiv:1802.08471, 2018
152018
Asymptotic equivalence of fixed-size and varying-size determinantal point processes
S Barthelmé, PO Amblard, N Tremblay
Bernoulli 25 (4B), 3555-3589, 2019
102019
Divide and conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference
S Barthelmé, N Chopin, V Cottet
Handbook of Approximate Bayesian Computation, 415-34, 2018
102018
The Poisson transform for unnormalised statistical models
S Barthelmé, N Chopin
Statistics and Computing 25 (4), 767-780, 2015
72015
A flexible Bayesian method for adaptive measurement in psychophysics
S Barthelmé, P Mamassian
arXiv preprint arXiv:0809.0387, 2008
72008
Reliable chiral recognition with an optoelectronic nose
P Maho, C Herrier, T Livache, G Rolland, P Comon, S Barthelmé
Biosensors and Bioelectronics 159, 112183, 2020
62020
Subsampling with k determinantal point processes for estimating statistics in large data sets
PO Amblard, S Barthelmé, N Tremblay
2018 IEEE Statistical Signal Processing Workshop (SSP), 313-317, 2018
42018
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