1 citations · 1 across the 8 of their papers we have counts for
7 papers · 1 filter
LoRe: Personalizing LLMs via Low-Rank Reward Modeling
Avinandan Bose, Zhihan Xiong, Yuejie Chi +3
Personalizing large language models (LLMs) to accommodate diverse user preferences is essential for enhancing alignment and user satisfaction. Traditional reinforcement learning fr…
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning
Avinandan Bose, Laurent Lessard, Maryam Fazel +1
The rise of foundation models fine-tuned on human feedback from potentially untrusted users has increased the risk of adversarial data poisoning, necessitating the study of robustn…
Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
Avinandan Bose, Zhihan Xiong, Aadirupa Saha +2
Reinforcement Learning from Human Feedback (RLHF) is currently the leading approach for aligning large language models with human preferences. Typically, these models rely on exten…
Offline Multi-task Transfer RL with Representational Penalization
Avinandan Bose, Simon Shaolei Du, Maryam Fazel
We study the problem of representation transfer in offline Reinforcement Learning (RL), where a learner has access to episodic data from a number of source tasks collected a priori…
Initializing Services in Interactive ML Systems for Diverse Users
Avinandan Bose, Mihaela Curmei, Daniel L. Jiang +4
This paper investigates ML systems serving a group of users, with multiple models/services, each aimed at specializing to a sub-group of users. We consider settings where upon depl…
Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees
Avinandan Bose, Arunesh Sinha, Tien Mai
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO so…