Search Results for author: Saikiran Bulusu

Found 6 papers, 0 papers with code

On Distributed and Asynchronous Sampling of Gaussian Processes for Sequential Binary Hypothesis Testing

no code implementations14 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.

Gaussian Processes

Ordered Transmission-based Detection in Distributed Networks in the Presence of Byzantines

no code implementations21 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.

LEARNING DISTRIBUTIONS GENERATED BY SINGLE-LAYER RELU NETWORKS IN THE PRESENCE OF ARBITRARY OUTLIERS

no code implementations29 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 $d$.

Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction

no code implementations1 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

Anomalous Example Detection in Deep Learning: A Survey

no code implementations16 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.

Anomaly Detection

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