6 citations · 6 across the 1 of their papers we have counts for
4 papers
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…
Polyak-Łojasiewicz inequality on the space of measures and convergence of mean-field birth-death processes
Linshan Liu, Mateusz B. Majka, Łukasz Szpruch
The Polyak-Lojasiewicz inequality (PLI) in is a natural condition for proving convergence of gradient descent algorithms. In the present paper, we study an analogue…
Multi-index Antithetic Stochastic Gradient Algorithm
Mateusz B. Majka, Marc Sabate-Vidales, Łukasz Szpruch
Stochastic Gradient Algorithms (SGAs) are ubiquitous in computational statistics, machine learning and optimisation. Recent years have brought an influx of interest in SGAs, and th…
Antithetic multilevel particle system sampling method for McKean-Vlasov SDEs
Łukasz Szpruch, Alvin Tse
Let , where denotes the space of square integrable probability measures, and consider a Borel-measurable function $Φ:\…