5 papers
Distributed and Rate-Adaptive Feature Compression
Aditya Deshmukh, Venugopal V. Veeravalli, Gunjan Verma
We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion…
Robust Mean Estimation in High Dimensions: An Outlier Fraction Agnostic and Efficient Algorithm
Aditya Deshmukh, Jing Liu, Venugopal V. Veeravalli
The problem of robust mean estimation in high dimensions is studied, in which a certain fraction (less than half) of the datapoints can be arbitrarily corrupted. Motivated by compr…
Robust Mean Estimation in High Dimensions via Minimization
Jing Liu, Aditya Deshmukh, Venugopal V. Veeravalli
We study the robust mean estimation problem in high dimensions, where fraction of the data points can be arbitrarily corrupted. Motivated by compressive sensing, we formula…
Sequential Controlled Sensing for Composite Multihypothesis Testing
Aditya Deshmukh, Srikrishna Bhashyam, Venugopal V. Veeravalli
The problem of multi-hypothesis testing with controlled sensing of observations is considered. The distribution of observations collected under each control is assumed to follow a…
Information Flow Optimization in Inference Networks
Aditya Deshmukh, Jing Liu, Venugopal V. Veeravalli +1
The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, c…