4 papers
Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
Simon Queric, Cédric Vincent-Cuaz, Charles Bouveyron +1
We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variational inference (MLVI) can be inte…
Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
Federica Granese, Benjamin Navet, Serena Villata +1
Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing…
Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams
Federica Granese, Serena Villata, Charles Bouveyron
Online topic models are unsupervised algorithms to identify latent topics in data streams that continuously evolve over time. Although these methods naturally align with real-world…
The Deep Latent Position Block Model For The Block Clustering And Latent Representation Of Networks
Rémi Boutin, Pierre Latouche, Charles Bouveyron
The increased quantity of data has led to a soaring use of networks to model relationships between different objects, represented as nodes. Since the number of nodes can be particu…