Évènements / S3

S3 Seminar
Évènements à venir

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 […]


Online estimation of elliptical distributions and their mixture: the component-wise information gradient method

Speaker — Jialun Zhou (IMS, Groupe Signal Image — CNRS, Université de Bordeaux) https://s3-seminar.github.io/seminars/jialun-zhou/ Abstract — Elliptically-Contoured Distributions (ECD) and its Mixture model (MECD) are highly versatile at modeling general, real-world probability distributions. They have therefore played a valuable role in computer vision, image processing, radar signal processing, and biomedical signal processing. Maximum likelihood estimation […]


Speeding up of kernel-based learning for high-order tensor

Speaker — Ouafae Karmouda (SIGMA team at CRIStAL laboratory, Lille, France) Abstract — Supervised learning is a major task to classify datasets. In our context, we are interested into classification from high-order tensors datasets. The “curse of dimensionality” states that the complexities in terms of storage and computation grow exponentially with the order. As a […]


Sampling rates for l1 synthesis

Speaker — Claire Boyer (Sorbonne Université) Abstract — This work investigates the problem of signal recovery from undersampled noisy sub-Gaussian measurements under the assumption of a synthesis-based sparsity model. Solving the l1-synthesis basis pursuit allows to simultaneously estimate a coefficient representation as well as the sought-for signal. However, due to linear dependencies within redundant dictionary […]


Efficient MCMC sampling via asymptotically exact data augmentation

Speaker — Maxime Vono (IRIT — INP-ENSEEIHT) https://s3-seminar.github.io/seminars/maxime-vono/ Abstract — Performing exact Bayesian inference for complex models is computationally intractable. Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the posterior distribution but are expensive for large datasets and high-dimensional models. A standard approach to mitigate this complexity consists in using subsampling techniques […]