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...
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...
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...
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...
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....
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...
