3 papers
stat.ML2026
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.…
stat.ML2025
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…
math.OC2024
Online Policy Selection for Inventory Problems
Massil Hihat, Adeline Fermanian
We tackle online inventory problems where at each time period the manager makes a replenishment decision based on partial historical information in order to meet demands and minimi…