11 papers
Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions
Prabhat K. Mishra, Joel A. Paulson, Richard D. Braatz
This article is devoted to providing a review of mathematical formulations in which Polynomial Chaos Theory (PCT) has been incorporated into stochastic model predictive control (SM…
BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search
Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson
Novelty search (NS) aims to uncover diverse system behaviors through simulation or experiment without requiring a pre-specified scalar objective. This capability is especially rele…
Rethinking Trust Region Bayesian Optimization in High Dimensions
Wei-Ting Tang, Joel A. Paulson
Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimization. However, inappropriate l…
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…
NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
Wei-Ting Tang, Akshay Kudva, Joel A. Paulson
Bayesian optimization (BO) is effective for expensive black-box problems but remains challenging in high dimensions. We propose NeST-BO, a curvature-aware local BO method that targ…