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  • Influence
Identifying Cognitive Radars - Inverse Reinforcement Learning Using Revealed Preferences
TLDR
We consider an inverse reinforcement learning problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and the algebraic Riccati equation. Expand
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Quickest Detection with Social Learning: Interaction of local and global decision makers
We consider how local and global decision policies interact in stopping time problems such as quickest time change detection. Individual agents make myopic local decisions via social learning, thatExpand
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Controllability of Network Opinion in Erdős-Rényi Graphs using Sparse Control Inputs
TLDR
This paper considers a social network modeled as an Erdos Renyi random graph. Expand
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Inverse Sequential Hypothesis Testing
TLDR
This paper considers a novel formulation of inverse reinforcement learning with behavioral economics constraints to address inverse sequential hypothesis testing (SHT) in Bayesian agents. Expand
A Low Power Robust Cascade Error Detection in High Level Function Verification-RCED
In this paper, a functional verification method gives the complete detection of errors and to resolve it.In the variation of timings probes are inserted to test the different cells automatically. ToExpand
Quickest Change Detection of Time Inconsistent Anticipatory Agents. Human-Sensor and Cyber-Physical Systems
  • V. Krishnamurthy
  • Computer Science, Engineering
  • IEEE Transactions on Signal Processing
  • 23 March 2020
TLDR
We show that the interaction between anticipatory agents and sequential quickest detection results in unusual (nonconvex) structure of the quickest change detection policy. Expand
A Markov Decision Process Approach to Active Meta Learning
TLDR
We propose active sample selection during training meta-models using multi-armed bandits (MAB) and Markov Decision Processes. Expand
Classification of Driving Behavior Events Utilizing Kinematic Classification and Machine Learning for Down Sampled Time Series Data
TLDR
The proliferation of connected cars globally has the potential to produce torrents of Big Data that will enable improvements in driver safety, new location based services, improvements in vehicle quality, and optimized vehicle designs. Expand
Behavioral Economics Approach to Interpretable Deep Image Classification. Rationally Inattentive Utility Maximization Explains Deep Image Classification
Are deep convolutional neural networks (CNNs) for image classification consistent with utility maximization behavior with information acquisition costs? This paper demonstrates the remarkable resultExpand
Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation
TLDR
We present a novel community-based graph model that represents the emergence of segregated echo chambers as a Markov bridge process. Expand