Journée Statistique et Informatique pour la Science des Données à Paris Saclay

Liste des contributions

1. Information-Theoretic Methods in Data Sciences: Model Uncertainty, Robustness and Model Drift

Prof. Pablo Piantanida (L2S/CentraleSupélec)

26/01/2022 10:20

Deep learning models are known to be bad at signalling failure: These probabilistic models tend to make predictions with high confidence, and this is problematic in real-world applications to critical systems such as healthcare, self-driving cars, among others, where there are considerable safety implications, or where there are discrepancies between the training data and data at testing time...

2. Bi-level Optimisation for Machine Learning

Prof. Thomas Moreau (INRIA Paris-Saclay)

26/01/2022 11:10

In recent years, bi-level optimization -- solving an optimization problem that depends on the results of another optimization problem -- has raised much interest in the machine learning community. This type of problem arises in many different fields, ranging from hyper-parameter optimization and data-augmentation to dictionary learning. A core question for such a problem is the estimation of...

4. Optimal Transport on Graph Data : Barycenters and Dictionary Learning

Prof. Rémi Flamary (CMAP/Ecole polytechnique)

26/01/2022 11:50

In recent years the Optimal Transport (OT) based Gromov-Wasserstein (GW) divergence has been investigated as a similarity measure between structured data expressed as distributions typically lying in different metric spaces, such as graphs with arbitrary sizes. In this talk, we will address the optimization problem inherent in the computation of GW and some of its recent extensions, namely the...

3. Machine Learning Competitions: a Meta-Learning Perspective

Prof. Isabelle Guyon (LISN/INRIA Tau)

26/01/2022 14:00

Our research aims at reducing the need for human expertise in the implementation of pattern recognition and modeling algorithms, including Deep Learning, in various fields of application (medicine, engineering, social sciences, physics), using multiple modalities (images, videos, text, time series, questionnaires). To that end, we organize scientific competitions (or challenges) in Automated...

5. Change-Point Detection in Dynamic Networks

Prof. Olga Klopp (CREST/ESSEC business school)

26/01/2022 14:50

Structural changes occur in dynamic networks quite frequently and its detection is an important question in many applications. In this talk we consider the problem of change point detection at a temporal sequence of partially observed networks. The goal is to test whether there is a change in the network parameters. Our approach is based on the Matrix CUSUM test statistic and allows growing...

6. Deep Learning Strategies for SAR Image Restoration

Prof. Florence Tupin (LTCI/Télécom Paris)

26/01/2022 15:30

SAR (Synthetic Aperture Radar) images are invaluable data for earth observation. They can be acquired at any time, regardless of the meteorological conditions, and provide information on the characteristics of the earth, its height and its possible movement thanks to the phase information of the backscattered electro-magnetic field.Due to the coherent imaging of the SAR sensors, images present...

7. Closing words

26/01/2022 16:10