56 citations · 88 across the 8 of their papers we have counts for
8 papers
Stochastic Control for Organ Donations: A Review
Xingyu Ren, Michael C. Fu, Steven I. Marcus
We review the literature on individual patient organ acceptance decision making by presenting a Markov Decision Process (MDP) model to formulate the organ acceptance decision proce…
Optimal Acceptance of Incompatible Kidneys
Xingyu Ren, Michael C. Fu, Steven I. Marcus
Incompatibility between patient and donor is a major barrier in kidney transplantation (KT). The increasing shortage of kidney donors has driven the development of desensitization…
Resilience to Denial-of-Service and Integrity Attacks: A Structured Systems Approach
Bhaskar Ramasubramanian, M. A. Rajan, M. Girish Chandra +2
The resilience of cyberphysical systems to denial-of-service (DoS) and integrity attacks is studied in this paper. The cyberphysical system is modeled as a linear structured system…
Notions of Centralized and Decentralized Opacity in Linear Systems
Bhaskar Ramasubramanian, Rance Cleaveland, Steven I. Marcus
We formulate notions of opacity for cyberphysical systems modeled as discrete-time linear time-invariant systems. A set of secret states is -ISO with respect to a set of nonsecr…
Random directions stochastic approximation with deterministic perturbations
Prashanth L A, Shalabh Bhatnagar, Nirav Bhavsar +2
We introduce deterministic perturbation schemes for the recently proposed random directions stochastic approximation (RDSA) [17], and propose new first-order and second-order algor…
Bisimulation and Hennessy-Milner Logic for Generalized Synchronization Trees
James Ferlez, Rance Cleaveland, Steve Marcus
In this work, we develop a generalization of Hennessy-Milner Logic (HML) for Generalized Synchronization Trees (GSTs) that we call Generalized Hennessy Milner Logic (GHML). Importa…
Bandit algorithms to emulate human decision making using probabilistic distortions
Ravi Kumar Kolla, Prashanth L. A., Aditya Gopalan +3
Motivated by models of human decision making proposed to explain commonly observed deviations from conventional expected value preferences, we formulate two stochastic multi-armed…
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Prashanth L. A., Cheng Jie, Michael Fu +2
Cumulative prospect theory (CPT) is known to model human decisions well, with substantial empirical evidence supporting this claim. CPT works by distorting probabilities and is mor…