132 citations · 317 across the 27 of their papers we have counts for
10 papers · 1 filter
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…
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery
Madhav Muthyala, Farshud Sorourifar, Joel A. Paulson
Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact represe…
Towards Efficient Quantum Computation of Molecular Ground State Energies using Bayesian Optimization with Priors over Surface Topology
Farshud Sorourifar, Mohamed Taha Rouabah, Nacer Eddine Belaloui +6
Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classic…
Bayesian Optimization Priors for Efficient Variational Quantum Algorithms
Farshud Sorourifar, Diana Chamaki, Norm M. Tubman +2
Quantum computers currently rely on a hybrid quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. Still, there are several challenges with V…
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…
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…
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization
Wei-Ting Tang, Joel A. Paulson
Bayesian optimization (BO) is a popular approach for optimizing expensive-to-evaluate black-box objective functions. An important challenge in BO is its application to high-dimensi…
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…
Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces
Farshud Sorourifar, Thomas Banker, Joel A. Paulson
Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive s…