Charles Bouveyron (Directeur de l’institut 3IA Côte d’Azur) a animé un Seminar@SystemX sur le thème « Deep latent variable models for unsupervised learning from interaction data », le 10 mai 2023 de 14h00 à 15h00 à l’amphi Peugeot /sc.046, Bâtiment Bouygues, CentraleSupélec ou en distanciel.
In this talk, we will focus on the problem of statistical learning with interaction data. This work is motivated by two real-world applications: the modeling and clustering of social networks, on the one hand, and of Pharmacovigilance data, on the other hand. To this end, we developed two model-based approaches. First, we propose the deep latent position model (DeepLPM), an end-to-end generative clustering approach which combines the widely used latent position model (LPM) for network analysis with a graph convolutional network (GCN) encoding strategy. An original estimation algorithm is introduced to integrate the explicit optimization of the posterior clustering probabilities via variational inference and the implicit optimization using stochastic gradient descent for graph reconstruction. Second, for the Pharmacovigilance problem, we introduce a latent block model for the dynamic co-clustering of count data streams with high sparsity. We assume that the observations follow a time and block dependent mixture of zero-inflated Poisson distributions, which combines two independent processes: a dynamic mixture of Poisson distributions and a time-dependent sparsity process. To model and detect abrupt changes in the dynamics of both clusters memberships and data sparsity, the mixing and sparsity proportions are modeled through systems of ordinary differential equations. The model inference relies on an original variational procedure whose maximization step trains recurrent neural networks in order to solve the dynamical systems. Numerical experiments on simulated data sets demonstrate the effectiveness of the proposed methodologies for the two problems.
Charles Bouveyron is Full Professor of Statistics with Université Côte d’Azur and the director of the Institut 3IA Côte d’Azur, one of the four French interdisciplinary institutes on Artificial Intelligence. He is the head of the Maasai research team, a joint team between INRIA and Université Côte d’Azur, gathering mathematicians and computer scientists for proposing innovative models and algorithms for Artificial Intelligence. Since 2019, he holds a chair on Artificial Intelligence at Institut 3IA Côte d’Azur on unsupervised learning with heterogenous data. His research interests include high-dimensional statistical learning, adaptive learning, statistical network analysis, learning from functional or complex data, with applications in medicine, image analysis and digital humanities. He has published extensively on these topics (more than 50 journal articles) and he is author of the monograph « Model-based Clustering and Classification for Data Science » (Cambridge University Press, 2019). He is the founding organizer of the series of workshops StatLearn. Previously, he worked at Université Paris Descartes (Full Professor, 2013-2017), Université Paris 1 Panthéon-Sorbonne (Ass. Professor, 2007-2013) and Acadia University (Postdoctoral researcher, 2006-2007). He received the Ph.D. degree in 2006 from Université Grenoble 1 (France) for his work on high-dimensional classification.