7 citations · 19 across the 21 of their papers we have counts for
5 papers · 1 filter
PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders
Chloé Hashimoto-Cullen, Ghislain Agoua, Benjamin Guedj +1
Forecasting time series accurately is critical for applications with complex data ranging from energy systems to healthcare and finance. Among current state of the art models, gene…
Theoretical Guarantees for SMC-Guided Diffusion Sampling
Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2
Post-hoc conditioning of pretrained diffusion models can be addressed using Sequential Monte Carlo (SMC) methods. By evolving an interacting particle system, SMC-guided diffusion s…
Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
Stanislas Strasman, Sobihan Surendran, Sylvain Le Corff
Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling…
On Forgetting and Stability of Score-based Generative models
Stanislas Strasman, Gabriel Cardoso, Sylvain Le Corff +2
Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provides quantitative bounds on the sampling…
Independent Component Discovery in Temporal Count Data
Alexandre Chaussard, Anna Bonnet, Sylvain Le Corff
Advances in data collection are producing growing volumes of temporal count observations, making adapted modeling increasingly necessary. In this work, we introduce a generative fr…