no code implementations • 14 Sep 2023 • Nandan Sriranga, Saikiran Bulusu, Baocheng Geng, Pramod K. Varshney
The distributed system is such that the sensors and the FC sample observations periodically, where the sampling times are not necessarily synchronous, i. e., the sampling times at different sensors and the FC may be different from each other.
no code implementations • 21 Jan 2022 • Chen Quan, Saikiran Bulusu, Baocheng Geng, Pramod K. Varshney
The ordered transmission (OT) scheme reduces the number of transmissions needed in the network to make the final decision, while it maintains the same probability of error as the system without using OT scheme.
no code implementations • 29 Sep 2021 • Saikiran Bulusu, Geethu Joseph, M. Cenk Gursoy, Pramod Varshney
Further, we prove that ${O}(\frac{1}{\epsilon p^4}\log\frac{d}{\delta})$ samples are sufficient for our algorithm to estimate the NN parameters within an error of $\epsilon$ with probability $1-\delta$ when the probability of a sample being uncorrupted is $p$ and the problem dimension is $d$.
no code implementations • 1 May 2020 • Prashant Khanduri, Pranay Sharma, Swatantra Kafle, Saikiran Bulusu, Ketan Rajawat, Pramod K. Varshney
In this work, we propose a distributed algorithm for stochastic non-convex optimization.
Optimization and Control Distributed, Parallel, and Cluster Computing
no code implementations • 16 Mar 2020 • Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications.
no code implementations • 12 Dec 2019 • Pranay Sharma, Swatantra Kafle, Prashant Khanduri, Saikiran Bulusu, Ketan Rajawat, Pramod K. Varshney
For online problems ($n$ unknown or infinite), we achieve the optimal IFO complexity $O(\epsilon^{-3/2})$.