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math.NA2026
On the computational cost of Stochastic Gradient Langevin Dynamics
Mateusz B. Majka, Tigran Nagapetyan, Łukasz Szpruch +2
Stochastic Gradient Langevin Dynamics (SGLD) reduces the cost of Langevin-based sampling by replacing full-dataset drift evaluations with mini-batch approximations, but the resulti…
math.OC2026
On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
Petra Lazić, Linshan Liu, Mateusz B. Majka
We consider a class of optimization problems on the space of probability measures motivated by the mean-field approach to studying neural networks. Such problems can be solved by c…