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stat.ML2026
Tightening the Score Matching Gap for Diffusion Models
Benjamin Dupuis, Tyler Farghly, Maxime Haddouche +2
Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution. Their training and evaluation primarily rely on an Evidence Lo…
stat.ML2026
Sequential Off-Policy Learning with Logarithmic Smoothing
Maxime Haddouche, Otmane Sakhi
Off-policy learning enables training policies from logged interaction data. Most prior work considers the batch setting, where a policy is learned from data generated by a single b…
stat.ML2026
Algorithm- and Data-Dependent Generalization Bounds for Diffusion Models
Benjamin Dupuis, Dario Shariatian, Maxime Haddouche +2
Score-based generative models (SGMs) have emerged as one of the most popular classes of generative models. A substantial body of work now exists on the analysis of SGMs, focusing e…