15 citations · 37 across the 11 of their papers we have counts for
15 papers
Ornstein-Uhlenbeck Is Hard to Beat, Yet Superlinear Drift Ships Lower Transport Costs
Attila Lovas, Lóránt Nagy
Brešar and Mijatović \cite{bresar2025} show that Ornstein--Uhlenbeck diffusion is hard to beat in forward convergence under assumptions that exclude superlinear drift. We instead t…
On the strong stability of ergodic iterations
László Györfi, Attila Lovas, Miklós Rásonyi
We revisit processes generated by iterated random functions driven by a stationary and ergodic sequence. Such a process is called strongly stable if a random initialization exists,…
Functional Central Limit Theorem and Strong Law of Large Numbers for Stochastic Gradient Langevin Dynamics
Attila Lovas, Miklós Rásonyi
We study the mixing properties of an important optimization algorithm of machine learning: the stochastic gradient Langevin dynamics (SGLD) with a fixed step size. The data stream…
Ergodic aspects of trading with threshold strategies
Attila Lovas, Miklós Rásonyi
To profit from price oscillations, investors frequently use threshold-type strategies where changes in the portfolio position are triggered by some indicators reaching prescribed l…
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,…
Ergodic theorems for queuing systems with dependent inter-arrival times
Attila Lovas, Miklós Rásonyi
We study a G/GI/1 single-server queuing model with i.i.d.\ service times that are independent of a stationary process of inter-arrival times. We show that the distribution of the w…