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cs.LG2026
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…
cs.LG2020★ 5 cited
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms
Attila Lovas, Iosif Lytras, Miklós Rásonyi +1
Artificial neural networks (ANNs) are typically highly nonlinear systems which are finely tuned via the optimization of their associated, non-convex loss functions. In many cases,…