5 papers
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.…
Convergence of Multi-Level Markov Chain Monte Carlo Adaptive Stochastic Gradient Algorithms
Antoine Godichon-Baggioni, Gabriel Lang, Sylvain Le Corff +2
Stochastic optimization in learning and inference often relies on Markov chain Monte Carlo (MCMC) to approximate gradients when exact computation is intractable. However, finite-ti…
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…
Wasserstein Convergence of Critically Damped Langevin Diffusions
Stanislas Strasman, Sobihan Surendran, Claire Boyer +3
Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications and benefit from strong theoretical guarantees. Rec…
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…