44 citations · 364 across the 85 of their papers we have counts for
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Lyapunov based Stochastic Stability of a Quantum Decision System for Human-Machine Interaction
Luke Snow, Shashwat Jain, Vikram Krishnamurthy
In mathematical psychology, decision makers are modeled using the Lindbladian equations from quantum mechanics to capture important human-centric features such as order effects and…
Lyapunov based Stochastic Stability of Human-Machine Interaction: A Quantum Decision System Approach
Luke Snow, Shashwat Jain, Vikram Krishnamurthy
In mathematical psychology, decision makers are modeled using the Lindbladian equations from quantum mechanics to capture important human-centric features such as order effects and…
Interval Dominance based Structural Results for Markov Decision Process
Vikram Krishnamurthy
Structural results impose sufficient conditions on the model parameters of a Markov decision process (MDP) so that the optimal policy is an increasing function of the underlying st…
Inverse Filtering for Hidden Markov Models with Applications to Counter-Adversarial Autonomous Systems
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
Bayesian filtering deals with computing the posterior distribution of the state of a stochastic dynamic system given noisy observations. In this paper, motivated by applications in…
What Did Your Adversary Believe? Optimal Filtering and Smoothing in Counter-Adversarial Autonomous Systems
Robert Mattila, Inês Lourenço, Vikram Krishnamurthy +2
We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our curre…
Convex Stochastic Dominance in Bayesian Localization, Filtering and Controlled Sensing POMDPs
Vikram Krishnamurthy
This paper provides conditions on the observation probability distribution in Bayesian localization and optimal filtering so that the conditional mean estimate satisfies convex sto…
New Sufficient Conditions for Lower Bounding the Optimal Policy of a POMDP using Lehmann Precision
Vikram Krishnamurthy
This paper provides new sufficient conditions so that the optimal policy of a partially observed Markov decision process (POMDP) can be lower bounded by a myopic policy. The two ne…
Multiple Stopping Time POMDPs: Structural Results & Application in Interactive Advertising in Social Media
Vikram Krishnamurthy, Anup Aprem, Sujay Bhatt
This paper considers a multiple stopping time problem for a Markov chain observed in noise, where a decision maker chooses at most L stopping times to maximize a cumulative objecti…
Computing monotone policies for Markov decision processes: a nearly-isotonic penalty approach
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
This paper discusses algorithms for solving Markov decision processes (MDPs) that have monotone optimal policies. We propose a two-stage alternating convex optimization scheme that…
Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. The main contributi…
POMDP Structural Results for Controlled Sensing
Vikram Krishnamurthy
This article provides a short review of some structural results in controlled sensing when the problem is formulated as a partially observed Markov decision process. In particular,…