5 papers
Scalable Policy-Based RL Algorithms for POMDPs
Ameya Anjarlekar, Rasoul Etesami, R Srikant
The continuous nature of belief states in POMDPs presents significant computational challenges in learning the optimal policy. In this paper, we consider an approach that solves a…
Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach
S. Rasoul Etesami, R. Srikant
We consider the problem of learning stable matchings with unknown preferences in a decentralized and uncoordinated manner, where "decentralized" means that players make decisions i…
Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
R. Srikant
We prove a non-asymptotic central limit theorem for vector-valued martingale differences using Stein's method, and use Poisson's equation to extend the result to functions of Marko…
Cascading Reinforcement Learning
Yihan Du, R. Srikant, Wei Chen
Cascading bandits have gained popularity in recent years due to their applicability to recommendation systems and online advertising. In the cascading bandit model, at each timeste…
Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression
Ameya Anjarlekar, Rasoul Etesami, R. Srikant
We address the challenge of solving machine learning tasks using data from privacy-sensitive sellers. Since the data is private, we design a data market that incentivizes sellers t…