9e Journée Statistique et Informatique pour la Science des Données à Paris-Saclay

Liste des Contributions

1. Reinforcement Learning, an Introduction and Some Results

Erwan Le Pennec (CMAP, École polytechnique, Institut Polytechnique de Paris)

03/04/2024 10:00

Reinforcement Learning is the "art" of learning how to act in an environment that is only observed through interactions.
In this talk, I will provide an introduction to this topic starting from the underlying probabilistic model, Markov Decision Process, describing how to learn a good policy (how to pick the actions) when this model is known and when it is unknown. I will stress the impact of...

2. Learning with Missing Values: Theoretical Insights and Application to Health Databases

Marine Le Morvan (INRIA, Saclay)

03/04/2024 11:20

Missing values are ubiquitous in many fields such as health, business or social sciences. To date, much of the literature on missing values has focused on imputation as well as inference with incomplete data. In contrast, supervised learning in the presence of missing values has received little attention. In this talk I will explain the challenges posed by missing values in regression and...

3. Unsupervised Alignment of Graphs and Embeddings: Fundamental Limits and Computational Methods

Luca Ganassali (Laboratoire de Mathématiques d'Orsay, Université Paris-Saclay)

03/04/2024 12:10

Aligning two (weighted or unweighted) graphs, or matching two clouds of high-dimensional embeddings, are fundamental problems in machine learning with applications across diverse domains such as natural language processing to computational biology. In this presentation I will introduce the graph alignment problem, which can be viewed as an average-case and noisy version of the graph...

4. Weak Signals: Machine-Learning Meets Extreme Value Theory

Stephan Clémençon (LTCI, Télécom Paris, Insitut Polytechnique de Paris)

03/04/2024 14:30

The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins. Its statistical recovery is an important step in learning problems involving observations far away from the center. In the common situation that the components of the vector have different...

5. Contextual Stochastic Bandits with Budget Constraints and Fairness Application

Gilles Stoltz (CNRS, LMO, Univ. Paris-Saclay)

03/04/2024 15:20

We review the setting and fundamental results of contextual stochastic bandits, where at each round some vector-valued context $x_t$ is observed and $K$ actions are available, each action a providing a stochastic reward with expectation given by some (partially unknown) function of $x_t$ and $a$. The aim is to maximize the cumulative rewards obtained, or equivalently, to minimize the regret....

6. Deep Learning in Medical Imaging: The Era of Foundation Models

Maria Vakalopoulou (Centralesupélec, Université Paris-Saclay)

03/04/2024 16:40

Deep learning methods have a very important role in medical imaging and it had gain a lot of attention the recent years. Currently, the community is working towards the development of large deep learning models that capture complex relations of the data and can address different tasks in a holistic way. In this talk, we will discuss about recent foundation models in medical imaging and we will...