4 papers
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…
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…
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Yilin Xie, Shiqiang Zhang, Joel A. Paulson +1
Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non-convex optimization problem itself. D…
Training Neural ODEs Using Fully Discretized Simultaneous Optimization
Mariia Shapovalova, Calvin Tsay
Neural Ordinary Differential Equations (Neural ODEs) represent continuous-time dynamics with neural networks, offering advancements for modeling and control tasks. However, trainin…