Научный руководитель лаборатории Эрик Мулин выступил на коллоквиуме факультета компьютерных наук
22 февраля 2018 года в рамках коллоквиума факультета компьютерных наук состоялось выступление научного руководителя Международной лаборатории стохастических алгоритмов и анализа много мерных данных Эрика Мулина с докладом на тему «Perturbed Proximal Gradient Algorithms».
Краткая аннотация доклада:
We study a version of the proximal gradient algorithm for which the gradient is intractable and is approximated by Monte Carlo methods (and in particular Markov Chain Monte Carlo). We derive conditions on the step size and the Monte Carlo batch size under which convergence is guaranteed: both increasing batch size and constant batch size are considered. We also derive non-asymptotic bounds for an averaged version. Our results cover both the cases of biased and unbiased Monte Carlo approximation. To support our findings, we discuss the inference of a sparse generalized linear model with random effect and the problem of learning the edge structure and parameters of sparse undirected graphical models.
Краткая аннотация доклада:
We study a version of the proximal gradient algorithm for which the gradient is intractable and is approximated by Monte Carlo methods (and in particular Markov Chain Monte Carlo). We derive conditions on the step size and the Monte Carlo batch size under which convergence is guaranteed: both increasing batch size and constant batch size are considered. We also derive non-asymptotic bounds for an averaged version. Our results cover both the cases of biased and unbiased Monte Carlo approximation. To support our findings, we discuss the inference of a sparse generalized linear model with random effect and the problem of learning the edge structure and parameters of sparse undirected graphical models.
Дата
22 февраля
2018
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