5 papers · 1 filter
Latent Guided Sampling for Combinatorial Optimization
Sobihan Surendran, Adeline Fermanian, Sylvain Le Corff
Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.…
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…
Diffusion posterior sampling for simulation-based inference in tall data settings
Julia Linhart, Gabriel Victorino Cardoso, Alexandre Gramfort +2
Identifying the parameters of a non-linear model that best explain observed data is a core task across scientific fields. When such models rely on complex simulators, evaluating th…
Theoretical Convergence Guarantees for Variational Autoencoders
Sobihan Surendran, Antoine Godichon-Baggioni, Sylvain Le Corff
Variational Autoencoders (VAE) are popular generative models used to sample from complex data distributions. Despite their empirical success in various machine learning tasks, sign…
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation
Sobihan Surendran, Antoine Godichon-Baggioni, Adeline Fermanian +1
Stochastic Gradient Descent (SGD) with adaptive steps is widely used to train deep neural networks and generative models. Most theoretical results assume that it is possible to obt…