3 papers
cs.LG2026
Flatness-Aware Stochastic Gradient Langevin Dynamics
Stefano Bruno, Youngsik Hwang, Jaehyeon An +2
Flatness of the loss landscape has been widely studied as an important perspective for understanding the behavior and generalization of deep learning algorithms. Motivated by this…
cs.LG2025
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
Stefano Bruno, Sotirios Sabanis
Score-based Generative Models (SGMs) approximate a data distribution by perturbing it with Gaussian noise and subsequently denoising it via a learned reverse diffusion process. The…
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…