8 papers
Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study
Weiqin Chen, Xinlong Du, Gesualdo Scutari +1
In-context reinforcement learning (ICRL) exploits the impressive in-context learning ability of large language models to zero-shot generalize to new, unseen tasks at inference time…
Narrow-Shell Stochasticity in Source-Sink Models of the Low Earth Orbit Environment
Jaewon Choi, Souvik Dhara, Harsha Honnappa
Deterministic source-sink models are widely used to assess the long-term evolution, capacity, and sustainability of the low Earth orbit (LEO) environment. These models propagate sh…
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…
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…
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…
Renewal Processes Represented as Doubly Stochastic Poisson Processes
Xinlong Du, Harsha Honnappa
This paper gives an elementary proof for the following theorem: a renewal process can be represented by a doubly-stochastic Poisson process (DSPP) if and only if the Laplace-Stielt…
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…