1 code implementation • 22 Oct 2019 • Ruben Ohana, Jonas Wacker, Jonathan Dong, Sébastien Marmin, Florent Krzakala, Maurizio Filippone, Laurent Daudet
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation.
no code implementations • 29 Oct 2018 • Sébastien Marmin, Maurizio Filippone
Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters.
no code implementations • 9 Sep 2016 • Sébastien Marmin, Clément Chevalier, David Ginsbourger
We deal with the efficient parallelization of Bayesian global optimization algorithms, and more specifically of those based on the expected improvement criterion and its variants.
no code implementations • 18 Mar 2015 • Sébastien Marmin, Clément Chevalier, David Ginsbourger
The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms.