2 papers
math.ST2025
kTULA: A Langevin sampling algorithm with improved KL bounds under super-linear log-gradients
Iosif Lytras, Sotirios Sabanis, Ying Zhang
Motivated by applications in deep learning, where the global Lipschitz continuity condition is often not satisfied, we examine the problem of sampling from distributions with super…
cs.LG2025
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates
Stefano Bruno, Ying Zhang, Dong-Young Lim +2
We provide full theoretical guarantees for the convergence behaviour of diffusion-based generative models under the assumption of strongly log-concave data distributions while our…