On Tamed Euler Approximations of SDEs Driven by Lévy Noise with Applications to Delay Equations. - Konstantinos Dareiotis; Chaman Kumar; Sotirios Sabanis (SIAM J. Numer. Anal., 2016)

Asymptotic Analysis of the LMS Algorithm with Momentum. - László Gerencsér; Balázs Csanád Csáji; Sotirios Sabanis (CDC, 2018)

Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization. - Ying Zhang 037; Ömer Deniz Akyildiz; Theo Damoulas; Sotirios Sabanis (CoRR, 2019)

On explicit order 1.5 approximations with varying coefficients: The case of super-linear diffusion coefficients. - Sotirios Sabanis; Ying Zhang 037 (J. Complex., 2019)

Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms. - Attila Lovas; Iosif Lytras; Miklós Rásonyi; Sotirios Sabanis (CoRR, 2020)

Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function. - Dong-Young Lim; Ariel Neufeld; Sotirios Sabanis; Ying Zhang 037 (CoRR, 2021)

On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case. - Ngoc Huy Chau; Éric Moulines; Miklós Rásonyi; Sotirios Sabanis; Ying Zhang 037 (SIAM J. Math. Data Sci., 2021)

Langevin dynamics based algorithm e-THεO POULA for stochastic optimization problems with discontinuous stochastic gradient. - Dong-Young Lim; Ariel Neufeld; Sotirios Sabanis; Ying Zhang 037 (CoRR, 2022)

Statistical Finite Elements via Langevin Dynamics. - Ömer Deniz Akyildiz; Connor Duffin; Sotirios Sabanis; Mark Girolami (SIAM/ASA J. Uncertain. Quantification, 2022)

Taming under isoperimetry. - Iosif Lytras; Sotirios Sabanis (CoRR, 2023)

Optimising portfolio diversification and dimensionality. - M. Barkhagen; Sergio García 001; Jacek Gondzio; Jörg Kalcsics; J. Kroeske; Sotirios Sabanis; A. Staal (J. Glob. Optim., 2023)

Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms. - Attila Lovas; Iosif Lytras; Miklós Rásonyi; Sotirios Sabanis (SIAM J. Math. Data Sci., 2023)

Kinetic Langevin MCMC sampling without gradient Lipschitz continuity - the strongly convex case. - Tim Johnston; Iosif Lytras; Sotirios Sabanis (J. Complex., 2024)

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization. - Ömer Deniz Akyildiz; Sotirios Sabanis (J. Mach. Learn. Res., 2024)

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks. - Dong-Young Lim; Sotirios Sabanis (J. Mach. Learn. Res., 2024)

A strongly monotonic polygonal Euler scheme. - Tim Johnston; Sotirios Sabanis (J. Complex., 2024)

Langevin Dynamics Based Algorithm e-THεO POULA for Stochastic Optimization Problems with Discontinuous Stochastic Gradient. - Dong-Young Lim; Ariel Neufeld; Sotirios Sabanis; Ying Zhang 037 (Math. Oper. Res., 2025)

On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates. - Stefano Bruno; Ying Zhang 037; Dongyoung Lim; Ömer Deniz Akyildiz; Sotirios Sabanis (Trans. Mach. Learn. Res., 2025)

Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients. - Stefano Bruno; Sotirios Sabanis (CoRR, 2025)

kTULA: A Langevin sampling algorithm with improved KL bounds under super-linear log-gradients. - Iosif Lytras; Sotirios Sabanis; Ying Zhang 037 (CoRR, 2025)