collaborators

10 papers

cs.LG2026

Latent-Augmented Discrete Diffusion Models

Dario Shariatian, Alain Durmus, Umut Simsekli +1

Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implementations typically rely on factored rev…

cs.LG2026

Non-Asymptotic Convergence of Discrete Diffusion Models: Masked and Random Walk dynamics

Giovanni Conforti, Alain Durmus, Le-Tuyet-Nhi Pham +1

Diffusion models for continuous state spaces based on Gaussian noising processes are now relatively well understood from both practical and theoretical perspectives. In contrast, r…

stat.AP2026

Test-then-Punish: A Statistical Approach to Repeated Games

Aymeric Capitaine, Antoine Scheid, Etienne Boursier +2

We study discounted infinitely repeated games in which players agree on a cooperative mixed action profile but, at each step, observe only the realized pure actions. This form of i…

cs.GT2026

Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes

Axel Benyamine, Julien Grand-Clément, Julien Grand-Clément +3

In this paper, we propose a general theory of ambiguity-averse MDPs, which treats the uncertain transition probabilities as random variables and evaluates a policy via a risk measu…

stat.ML2025

Iterative Tilting for Diffusion Fine-Tuning

Jean Pachebat, Giovanni Conforti, Alain Durmus +1

We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt in…

stat.ML2025

Exponential Convergence Guarantees for Iterative Markovian Fitting

Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus

The Schrödinger Bridge (SB) problem has become a fundamental tool in computational optimal transport and generative modeling. To address this problem, ideal methods such as Iterat…