Full Professor, École Polytechnique
3 papers at NeurIPS 2025
We prove algorithm- and data-dependent upper bounds on the generalization error of diffusion models by using tools from statistical learning theory
We introduce a data-centric estimator of the squared error of usual precision matrix estimates, both in the non-augmented case and the augmented case. Furthermore, we give non-asymptotic guarantees on our estimate.
