16 citations · 27 across the 26 of their papers we have counts for
7 papers · 1 filter
Score-Based Quickest Change Detection and Fault Identification for Multi-Stream Signals
Wuxia Chen, Sean Moushegian, Vahid Tarokh +1
This paper introduces an approach to multi-stream quickest change detection and fault isolation for unnormalized and score-based statistical models. Traditional optimal algorithms…
Large Deviation Analysis of Score-based Hypothesis Testing
Enmao Diao, Taposh Banerjee, Vahid Tarokh
Score-based statistical models play an important role in modern machine learning, statistics, and signal processing. For hypothesis testing, a score-based hypothesis test is propos…
Modeling and Quickest Detection of a Rapidly Approaching Object
Tim Brucks, Taposh Banerjee, Rahul Mishra
The problem of detecting the presence of a signal that can lead to a disaster is studied. A decision-maker collects data sequentially over time. At some point in time, called the c…
A Bayesian Theory of Change Detection in Statistically Periodic Random Processes
Taposh Banerjee, Prudhvi Gurram, Gene Whipps
A new class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes is defined to capture periodically varying statistical behavior…
Minimax-Optimal Algorithms for Detecting Changes in Statistically Periodic Random Processes
Taposh Banerjee, Prudhvi Gurram, Gene Whipps
Theory and algorithms are developed for detecting changes in the distribution of statistically periodic random processes. The statistical periodicity is modeled using independent a…
Sequential Detection of Regime Changes in Neural Data
Taposh Banerjee, Stephen Allsop, Kay M. Tye +2
The problem of detecting changes in firing patterns in neural data is studied. The problem is formulated as a quickest change detection problem. Important algorithms from the liter…
Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data
Taposh Banerjee, Gene Whipps, Prudhvi Gurram +1
A framework is proposed to detect anomalies in multi-modal data. A deep neural network-based object detector is employed to extract counts of objects and sub-events from the data.…