Évènements / UQSay

UQSay Seminar
Évènements à venir

UQSay #39

The thirty-ninth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, December 16, 2021. 2–3 PM — Gianni Franchi (U2IS, ENSTA Paris) — [slides] Encoding the latent posterior of Bayesian neural networks for Uncertainty Quantification Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for […]

16/12/2021

UQSay #38

The thirty-eighth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, December 2, 2021. 2–3 PM — Luc Pronzato (CNRS, Univ. Côte d’Azur) — [slides] Maximum Mean Discrepancy, Bayesian integration and kernel herding for space-filling design A standard objective in computer experiments is to predict/interpolate the behaviour of an […]

02/12/2021

UQSay #37

The thirty-seventh UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, November 18, 2021. 2–3 PM — Toni Karvonen (University of Helsinki) — [slides] Parameter estimation in Gaussian process regression for deterministic functions In fields such as kriging, modelling of computer experiments, and probabilistic numerical computation, Gaussian process (GP) […]

18/11/2021

UQSay #36

The thirty-sixth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, November 4, 2021. 2–3 PM — Thomas Santner (Ohio State University) — [slides] Using Combined Physical and Computer Experiments to Solve Bioengineering Problems Bioengineering seeks to solve problems at the confluence of Engineering and Biology. Classical Bioengineering applications […]

04/11/2021

UQSay #35

The thirty-fifth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, October 21, 2021. 3–4 PM — Polina Kirichenko (New York University) — [slides] Scaling Bayesian Deep Learning: Subspace Inference Bayesian methods can provide full-predictive distributions and well-calibrated uncertainties in modern deep learning. The Bayesian approach is especially relevant […]

21/10/2021