1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
How Many Initial Points Does Bayesian Optimization Need?
Mujin Cheon, James Odgers, Dong-Yeun Koh +1
Bayesian Optimization (BO) generally begins with an initialization phase: a batch of uninformed evaluations. The choice of remains largely heuristic, and we empirically…
cs.LG2026
BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization
Joon-Hyun Park, Mujin Cheon, Jeongsu Wi +1
The performance of Bayesian optimization (BO), a highly sample-efficient method for expensive black-box problems, is critically governed by the selection of its hyperparameters, in…
cs.LG2026★ 1 cited
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
Mujin Cheon, Jay H. Lee, Dong-Yeun Koh +1
To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated promising results in recent years. H…