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

Ergodic theorems for queuing systems with dependent inter-arrival times. - Attila Lovas; Miklós Rásonyi (Oper. Res. Lett., 2021)

Functional Central Limit Theorem and Strong Law of Large Numbers for Stochastic Gradient Langevin Dynamics. - Attila Lovas; Miklós Rásonyi (CoRR, 2022)

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)

Transition of α-mixing in Random Iterations with Applications in Queuing Theory. - Attila Lovas (CoRR, 2024)

Ergodic aspects of trading with threshold strategies. - Attila Lovas; Miklós Rásonyi (Ann. Oper. Res., 2024)

On the strong stability of ergodic iterations. - László Györfi; Attila Lovas; Miklós Rásonyi (J. Appl. Probab., 2025)