1 citations · 4 across the 7 of their papers we have counts for
7 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…
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…
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…
A Quantile Neural Network Framework for Two-stage Stochastic Optimization
Antonio Alcántara, Carlos Ruiz, Calvin Tsay
Two-stage stochastic programming is a popular framework for optimization under uncertainty, where decision variables are split between first-stage decisions, and second-stage (or r…
Bayesian optimization as a flexible and efficient design framework for sustainable process systems
Joel A. Paulson, Calvin Tsay
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engine…
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
Tom McDonald, Calvin Tsay, Artur M. Schweidtmann +1
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution o…