2 citations · 3 across the 19 of their papers we have counts for
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The Tamed Subgradient Unadjusted Langevin Algorithm beyond Convexity
Iosif Lytras, Nikolaos Makras, Sotirios Sabanis
We study the problem of sampling from target distributions whose potentials are simultaneously non-smooth, subject to superlinear gradient growth, and non-convex. We introduce the…
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…
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…
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…