Anit Kumar Sahu

TL;DR: A novel data-driven approach for indoor thermal model inference is presented, which combines an Autoregressive Moving Average with eXogenous inputs model (ARMAX) with a Normalized Mutual Information scheme (NMI).

TL;DR: In this article, a simple and efficient Bayesian Optimization (BO) based approach for developing black-box adversarial attacks is proposed. But the method is limited to output label (hard-label) to a queried data input.

TL;DR: Two distributed recursive generalized likelihood ratio test type algorithms of the \emph{consensus+innovations} form are proposed, namely $\mathcal{CILRT}$ and $\mathCal{CIGLRT}$, in which the agents estimate the underlying parameter and in parallel also update their test decision statistics by simultaneously processing the latest local sensed information and information obtained from neighboring agents.

TL;DR: Distributed stochastic optimization methods for zeroth order strongly convex optimization that are based on an adaptive probabilistic sparsifying communications protocol are developed and established with the proposed method O(1/(Ccomm)2/3-ζ) mean square error (MSE) convergence rates.

TL;DR: It is rigorously proved that CREDO−Nℒ$\mathcal {CREDO-NL}$ achieves significantly faster mean squared error rates in terms of the elapsed communication cost over existing alternatives, and the considered simulation experiments show communication savings by at least an order of magnitude.