5 papers
Zeroth-Order Non-smooth Non-convex Optimization via Gaussian Smoothing
Anik Kumar Paul, Shalabh Bhatnagar
This paper addresses stochastic optimization of Lipschitz-continuous, nonsmooth and nonconvex objectives over compact convex sets, where only noisy function evaluations are availab…
Stochastic Mirror Descent under Iterate-Dependent Markov Noise: Analysis in the Asymptotic and Finite Time Regimes
Anik Kumar Paul, Shalabh Bhatnagar
We study a stochastic optimization problem in which the sampling distribution depends on the decision variable, and the available samples are generated through an iterate-dependent…
Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimates
Anik Kumar Paul, Nibedita Roy, Nagesh Talagani +3
We propose FAR-SIGN (Fully Asynchronous Robust optimization via SIGNed directional projections) for adversary-resilient learning in parameter-server--worker systems. FAR-SIGN achie…
Stochastic Recursive Inclusions under Biased Perturbations: An Input-to-State Stability Perspective
Anik Kumar Paul, Karthik Shenoy, Arun D. Mahindrakar
This paper investigates the asymptotic behavior of stochastic recursive inclusions in the presence of non-zero, non-diminishing bias, a setting that frequently arises in zeroth-ord…
Federated Learning: A Stochastic Approximation Approach
Srihari P, Anik Kumar Paul, Bharath Bhikkaji
This paper considers the Federated learning (FL) in a stochastic approximation (SA) framework. Here, each client trains a local model using its dataset and…