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Found 4 papers, 0 papers with code
On the strong stability of ergodic iterations
We revisit processes generated by iterated random functions driven by a stationary and ergodic sequence.
Functional Central Limit Theorem and Strong Law of Large Numbers for Stochastic Gradient Langevin Dynamics
We study the mixing properties of an important optimization algorithm of machine learning: the stochastic gradient Langevin dynamics (SGLD) with a fixed step size.
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms
We offer a new learning algorithm based on an appropriately constructed variant of the popular stochastic gradient Langevin dynamics (SGLD), which is called tamed unadjusted stochastic Langevin algorithm (TUSLA).
Markov chains in random environment with applications in queueing theory and machine learning
We prove the existence of limiting distributions for a large class of Markov chains on a general state space in a random environment.
