Séance Séminaire

Séminaire de Probabilités et Statistique

Unsupervised statistical learning with latent block models on dynamic discrete data

In our interconnected world, we constantly generate vast and diverse data, necessitating automated methods for detection, synthesis, and understanding. Over the years, statistical learning has evolved to address these data challenges, with unsupervised learning being pivotal in uncovering patterns without predefined labels. In particular, clustering is a valuable technique for summarizing high-dimensional data by grouping similar observations based on shared characteristics. Extending this approach to simultaneously group observations and features, known as co-clustering, reveals intricate relationships among data. Within this framework, we delve into the exploration of two dynamic co-clustering models specifically designed for count data. These models integrate systems of ordinary differential equations, enabling them to adeptly capture sudden shifts in group membership and data sparsity as time progresses, with the second model boasting the capability to function in real-time on data streams. These innovations hold practical implications in the field of pharmacovigilance, a critical domain dedicated to the continuous monitoring and assessment of the safety of medical products.

Séminaire en salle 109, également retransmis sur zoom : https://umontpellier-fr.zoom.us/j/94087408185