2 citations · 5 across the 25 of their papers we have counts for
9 papers · 1 filter
High-Probability Nash Regret for Decentralized Learning in Markov -Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games
S. Rasoul Etesami
We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov -potential games. We develop KL-pro…
GUARD: Guided Unlearning and Retention via Data Attribution for Large Language Models
Peizhi Niu, Evelyn Ma, Huiting Zhou +4
Unlearning in large language models is becoming increasingly important due to regulatory compliance, copyright protection, and privacy concerns. However, a key challenge in LLM unl…
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
Evelyn Ma, Chao Pan, Rasoul Etesami +2
The performance of Transfer Learning (TL) heavily relies on effective pretraining, which demands large datasets and substantial computational resources. As a result, executing TL i…
F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data
Zexing Xu, Linjun Zhang, Sitan Yang +4
Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data f…
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…
Online Reinforcement Learning in Markov Decision Process Using Linear Programming
Vincent Leon, S. Rasoul Etesami
We consider online reinforcement learning in episodic Markov decision process (MDP) with unknown transition function and stochastic rewards drawn from some fixed but unknown distri…