5 papers
Many Preferences, Few Policies: Towards Scalable Language Model Personalization
Cheol Woo Kim, Jai Moondra, Roozbeh Nahavandi +3
The holy grail of LLM personalization is a single LLM for each user, perfectly aligned with that user's preferences. However, maintaining a separate LLM per user is impractical due…
Stochastic Function Certification with Correlations
Rohan Ghuge, Jai Moondra, Mohit Singh
We study the Stochastic Boolean Function Certification (SBFC) problem, where we are given Bernoulli random variables on a ground set of elements with…
Improved Regret Guarantees for Online Mirror Descent using a Portfolio of Mirror Maps
Swati Gupta, Jai Moondra, Mohit Singh
OMD and its variants give a flexible framework for OCO where the performance depends crucially on the choice of the mirror map. While the geometries underlying OPGD and OEG, both s…
Provably Small Portfolios for Multiobjective Optimization with Application to Subsidized Facility Location
Swati Gupta, Jai Moondra, Mohit Singh
Many multiobjective real-world problems, such as facility location and bus routing, become more complex when optimizing the priorities of multiple stakeholders. These are often mod…
Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning
Cheol Woo Kim, Jai Moondra, Shresth Verma +4
In many real-world applications of reinforcement learning (RL), deployed policies have varied impacts on different stakeholders, creating challenges in reaching consensus on how to…