Finite Sample and Large Deviations Analysis of Stochastic Gradient Algorithm with Correlated Noise

This paper focuses on finite sample analysis for stochastic gradient algorithms. The motivation stems from a vast varieties of applications. In particular, the recent advances on stochastic optimization in conjunction with machine learning have opened up new domains. A particular emphasis of the learning community requires us taking a careful look at of the finite sample analysis. Well, it is well known that stochastic gradient algorithms or stochastic approximation algorithms are normally concentrated on dealing with asymptotic properties of the recursive algorithms. However, the learning community placed more effort for carrying out analysis of finite sample properties of the recursive algorithms; see for example,... and references therein.