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Found 6 papers, 0 papers with code
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation
This paper provides a comprehensive non-asymptotic analysis of SGD with biased gradients and adaptive steps for convex and non-convex smooth functions.
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
This paper addresses second-order stochastic optimization for estimating the minimizer of a convex function written as an expectation.
Online stochastic Newton methods for estimating the geometric median and applications
In the context of large samples, a small number of individuals might spoil basic statistical indicators like the mean.
Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications
In stochastic optimization, a common tool to deal sequentially with large sample is to consider the well-known stochastic gradient algorithm.
Learning from time-dependent streaming data with online stochastic algorithms
This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data
We provide non-asymptotic convergence rates of various gradient-based algorithms; this includes the famous Stochastic Gradient (SG) descent (a. k. a.
