4 papers · 1 filter
Chance-Constrained Personalized LLM Routing with Online Preference Learning
Ameya Anjarlekar, Rasoul Etesami, R Srikant
We study personalized LLM routing, where a router selects among heterogeneous models with different costs and capabilities. The router must minimize cumulative inference cost while…
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…
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…