5 papers
Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
Imon Banerjee, Harsha Honnappa, Vinayak Rao
In this work, we study a natural nonparametric estimator of the transition probability matrices of a finite controlled Markov chain. We consider an offline setting with a fixed dat…
A stochastic optimization algorithm for revenue maximization in a service system with balking customers
Shreehari Anand Bodas, Harsha Honnappa, Michel Mandjes +1
This paper analyzes a service system modeled as a single-server queue, in which the service provider aims to dynamically maximize the expected revenue per unit of time. This is ach…
Neural Diffusion Intensity Models for Point Process Data
Xinlong Du, Harsha Honnappa, Vinayak Rao
Cox processes model overdispersed point process data via a latent stochastic intensity, but both nonparametric estimation of the intensity model and posterior inference over intens…
Drift Optimization of Regulated Stochastic Models Using Sample Average Approximation
Zihe Zhou, Harsha Honnappa, Raghu Pasupathy
This paper introduces a drift optimization model of stochastic optimization problems driven by regulated stochastic processes. A broad range of problems across operations research,…
Adaptive Estimation of the Transition Density of Controlled Markov Chains
Imon Banerjee, Vinayak Rao, Harsha Honnappa
Estimating the transition dynamics of controlled Markov chains is crucial in fields such as time series analysis, reinforcement learning, and system exploration. Traditional non-pa…