5 citations · 12 across the 8 of their papers we have counts for
8 papers
Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration
Henry Shaowu Yuchi, Michael Grosskopf, Aman Sharma +4
Gaussian processes are widely used for surrogate modeling in computer experiments, which often produce numerous intermediate variables that are not explicitly used in standard cali…
Stochastic Average Model Methods
Matt Menickelly, Stefan M. Wild
We consider the solution of finite-sum minimization problems, such as those appearing in nonlinear least-squares or general empirical risk minimization problems. We are motivated b…
Modeling Approaches for Addressing Simple Unrelaxable Constraints with Unconstrained Optimization Methods
Misha Padidar, Jeffrey Larson, Stefan M. Wild
We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated.…
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization
Raghu Bollapragada, Stefan M. Wild
We consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to r…
Derivative-Free Optimization of a Rapid-Cycling Synchrotron
Jeffrey S. Eldred, Jeffrey Larson, Misha Padidar +2
We develop and solve a constrained optimization model to identify an integrable optics rapid-cycling synchrotron lattice design that performs well in several capacities. Our model…
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control
S. Ashwin Renganathan, Jeffrey Larson, Stefan M. Wild
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate stochastic oracle. We are interested in the decision that maximizes the oracl…
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization
S. Ashwin Renganathan, Jeffrey Larson, Stefan Wild
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate oracle. We are interested in the decision that maximizes the oracle at a fini…
Randomized Derivative-Free Optimization of Noisy Convex Functions
Ruobing Chen, Stefan Wild
We propose STARS, a randomized derivative-free algorithm for unconstrained optimization when the function evaluations are contaminated with random noise. STARS takes dynamic, noise…