3 papers
cs.LG2025
Collaborative Bayesian Optimization via Wasserstein Barycenters
Donglin Zhan, Haoting Zhang, Rhonda Righter +2
Motivated by the growing need for black-box optimization and data privacy, we introduce a collaborative Bayesian optimization (BO) framework that addresses both of these challenges…
math.OC2025
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
Donglin Zhan, Leonardo F. Toso, James Anderson
We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically assumes that all available tasks are equ…
cs.LG2024
Data-Efficient and Robust Task Selection for Meta-Learning
Donglin Zhan, James Anderson
Meta-learning methods typically learn tasks under the assumption that all tasks are equally important. However, this assumption is often not valid. In real-world applications, task…