Friday, March 1, 2019

UQSay scope

The scope of UQSay seminars will include
  • UQ methodology in a broad sense
    • propagation of uncertainty in numerical models,
    • sensitivity analysis,
    • design and analysis of computer experiments,
    • surrogate models, mutli-fidelity,
    • reliability analysis, inversion, (Bayesian) optimization,
    • representations and elicitation of uncertainty,
    • verification and validation of numerical models,
    • data assimilation, calibration,
    • any topic in proba / stats / numerical analysis related to the above,
    • ...
  • and (of course) applications
    • engineering,
    • physical sciences,
    • biology,
    • environment,
    • ... 
(and by the way: UQ stands for "Uncertainty Quantification" 😉)

Tuesday, February 19, 2019

UQSay #01

The first UQSay seminar, organized by L2S, will take place in the afternoon of March 21, 2019, at CentraleSupelec Paris-Saclay (Eiffel building, amphi IV).  We will have two talks:

14h - Mickaël Binois (INRIA Sophia-Antipolis)

Heteroskedastic Gaussian processes for simulation experiments

An increasing number of time-consuming simulators exhibit a complex noise structure that depends on the inputs. To conduct studies with limited budgets of evaluations, new surrogate methods are required to model simultaneously the mean and variance fields. To this end, we present recent advances in Gaussian process modeling with input-dependent noise. First, we describe a simple, yet efficient, joint modeling framework that rely on replication for both speed and accuracy. Then we tackle the issue of leveraging replication and exploration in a sequential manner for various goals, such as obtaining a globally accurate model, for optimization, contour finding, and active subspace estimation. We illustrate these on applications coming from epidemiology and inventory management.

Ref :

15h - François Bachoc (IMT, Toulouse)

Gaussian process regression model for distribution inputs

Monge-Kantorovich distances, otherwise known as Wasserstein distances, have received a growing attention in statistics and machine learning as a powerful discrepancy measure for probability distributions. In this paper, we focus on forecasting a Gaussian process indexed by probability distributions. For this, we provide a family of positive definite kernels built using transportation based distances. We provide asymptotic results for covariance function estimation and prediction. We also provide numerical comparisons with other forecast methods based on distribution inputs.

Ref :

Organizers : Julien Bect (L2S) and Emmanuel Vazquez (L2S).

No registration is needed, but an email would be appreciated if you intend to come.