5 papers
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…
LLM Unlearning using Gradient Ratio-Based Influence Estimation and Noise Injection
Ameya Anjarlekar, Sandeep Pombra
The growing legal and ethical scrutiny of large language models (LLMs) necessitates effective machine unlearning, particularly for sensitive or unauthorized data. Existing empirica…
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…
A Weighted Generalized Coherence Approach for Sensing Matrix Design
Ameya Anjarlekar, Ajit Rajwade
As compared to using randomly generated sensing matrices, optimizing the sensing matrix w.r.t. a carefully designed criterion is known to lead to better quality signal recovery giv…