4 citations · 5 across the 12 of their papers we have counts for
13 papers
Optimal Threshold Type Policies for Partially Observable Restless Bandits
Anu Krishna, Rahul Meshram, Kesav Ram Kaza
We study a finite-state partially observable restless multi-armed bandit (PO-RMAB) motivated by resource-constrained wildlife monitoring. The underlying condition of each location…
Lagrangian Relaxation for Multi-Action Partially Observable Restless Bandits: Heuristic Policies and Indexability
Rahul Meshram, Kesav Kaza
Partially observable restless multi-armed bandits have found numerous applications including in recommendation systems, communication systems, public healthcare outreach systems, a…
Hierarchical Decentralized Stochastic Control for Cyber-Physical Systems
Kesav Kaza, Ramachandran Anantharaman, Rahul Meshram
This paper introduces a two-timescale hierarchical decentralized control architecture for Cyber-Physical Systems (CPS). The system consists of a global controller (GC), and N local…
Task load dependent decision referrals for joint binary classification in human-automation teams
Kesav Kaza, Jerome Le Ny, Aditya Mahajan
We consider the problem of optimal decision referrals in human-automation teams performing binary classification tasks. The automation, which includes a pre-trained classifier, obs…
An Intent Modeling and Inference Framework for Autonomous and Remotely Piloted Aerial Systems
Kesav Kaza, Varun Mehta, Hamid Azad +2
An intent modelling and inference framework is presented to assist the defense planning for protecting a geo-fence against unauthorized flights. First, a novel mathematical definit…
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits
Rahul Meshram, Kesav Kaza
Restless multi-armed bandits with partially observable states has applications in communication systems, age of information and recommendation systems. In this paper, we study mult…
Monte Carlo Rollout Policy for Recommendation Systems with Dynamic User Behavior
Rahul Meshram, Kesav Kaza
We model online recommendation systems using the hidden Markov multi-state restless multi-armed bandit problem. To solve this we present Monte Carlo rollout policy. We illustrate n…
Simulation Based Algorithms for Markov Decision Processes and Multi-Action Restless Bandits
Rahul Meshram, Kesav Kaza
We consider multi-dimensional Markov decision processes and formulate a long term discounted reward optimization problem. Two simulation based algorithms---Monte Carlo rollout poli…
Online repeated posted price auctions with a demand side platform
Rahul Meshram, Kesav Kaza
We consider an online ad network problem in which an ad exchange auctions ad slots and intermediaries called demand side platforms (DSPs) buy these ad slots for their clients (adve…
Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems
Kesav Kaza, Rahul Meshram, Varun Mehta +1
This paper studies a class of constrained restless multi-armed bandits (CRMAB). The constraints are in the form of time varying set of actions (set of available arms). This variati…
Restless Bandits with Constrained Arms: Applications in Social and Information Networks
Varun Mehta, Rahul Meshram, Kesav Kaza +1
We study a problem of information gathering in a social network with dynamically available sources and time varying quality of information. We formulate this problem as a restless…
Sequential Decision Making with Limited Observation Capability: Application to Wireless Networks
Kesav Kaza, Rahul Meshram, Varun Mehta +1
This work studies a generalized class of restless multi-armed bandits with hidden states and allow cumulative feedback, as opposed to the conventional instantaneous feedback. We ca…
Multi-armed Bandits with Constrained Arms and Hidden States
Varun Mehta, Rahul Meshram, Kesav Kaza +1
The problem of rested and restless multi-armed bandits with constrained availability of arms is considered. The states of arms evolve in Markovian manner and the exact states are h…