Évènements / S3

S3 Seminar
Évènements à venir

Towards GNSS High Precision Navigation: Manifolds and Robust Statistics

Speaker — Daniel Medina (Institute of Communications and Navigation, German Aerospace Center) Abstract — Navigation information is an essential element for the operation of robotics platforms and intelligent transportation systems. Global Navigation Satellite Systems (GNSS) have established as the cornerstone for outdoor navigation, providing all-weather, all-time positioning and timing at a worldwide scale. The use […]


Probabilistic PCA for heterogeneous-quality data

Speaker — David Hong (Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, US) Abstract — Principal component analysis (PCA) is a workhorse method for identifying low-dimensional (i.e., low-rank) structure in noisy data and is ubiquitous in signal processing. However, it estimates the underlying low-rank structure sub-optimally when the samples have heterogeneous quality, […]


UQSay #46

The forty-sixth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, April 21, 2022. 2–3 PM — Antoine Ajenjo (EDF R&D, FEMTO-ST) — [slides] Robustness assessment of reliability-oriented quantities of interest for the safety of industrial structures using the info-gap framework Structural reliability is of particular interest for risk-sensitive […]


UQSay #42

The forty-second UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, February 3, 2022. 2–3 PM — Pietro Congedo (Inria, CMAP, Ecole Polytechnique, IPP) — [slides] Optimization under Uncertainty of Large Dimensional Problems using Quantile Bayesian Regression Robust optimization strategies typically aim at minimizing some statistics of the uncertain […]


Robust low-rank covariance matrix estimation with missing values and application to classification problems

Speaker — Alexandre Hippert-Ferrer (L2S, CentraleSupelec, Univ. Paris-Saclay) Abstract — Missing values are inherent to real-world data sets. Statistical learning problems often require the estimation of parameters as the mean or the covariance matrix (CM). If the data is incomplete, new estimation methodologies need to be designed depending on the data distribution and the missingness […]