1 citations · 1 across the 9 of their papers we have counts for
9 papers
Stochastic Approximation in Banach Spaces Without Geometric Constraints
Rajeeva Laxman Karandikar, B V Rao
The thrust of this article is to show that on all Banach spaces, stochastic approximation holds when the noise sequence is an i.i.d. sequence with mean 0, without imposing any cond…
Excessive use, ill use and misuse of Bibliometrics
Rajeeva Laxman Karandikar
Impact factor, H-index, citation index, and such other indices have been playing an increasing role in scientific assessment of institutions, researchers, allocation of research fu…
Revisiting Stochastic Approximation and Stochastic Gradient Descent
Rajeeva Laxman Karandikar, Bhamidi Visweswara Rao, Mathukumalli Vidyasagar
In this paper, we introduce a new approach to proving the convergence of the Stochastic Approximation (SA) and the Stochastic Gradient Descent (SGD) algorithms. The new approach is…
Stochastic approximation in infinite dimensions
Rajeeva Laxman Karandikar, Bhamidi V Rao
Stochastic Approximation (SA) was introduced in the early 1950's and has been an active area of research for several decades. While the initial focus was on statistical questions,…
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications
Rajeeva L. Karandikar, M. Vidyasagar
In this paper, we study the convergence properties of the Stochastic Gradient Descent (SGD) method for finding a stationary point of a given objective function . The obje…
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning
Rajeeva L. Karandikar, M. Vidyasagar
We begin by briefly surveying some results on the convergence of the Stochastic Gradient Descent (SGD) Method, proved in a companion paper by the present authors. These results are…