66 citations · 84 across the 19 of their papers we have counts for
4 papers · 1 filter
An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
Wei-Ting Tang, Akshay Kudva, Calvin Tsay +1
We study the deterministic global optimization of trained Gaussian process posterior mean functions over hyperrectangular domains. Although the posterior mean function has a compac…
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…
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
Yen-An Lu, Wei-Shou Hu, Joel A. Paulson +1
This work addresses data-driven inverse optimization (IO), where the goal is to estimate unknown parameters in an optimization model from observed decisions that can be assumed to…
Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers
Dinesh Krishnamoorthy, Joel A. Paulson
Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off betw…