143 citations · 145 across the 3 of their papers we have counts for
6 papers
Scalable Constrained Bayesian Optimization
David Eriksson, Matthias Poloczek
The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and engineering. These problems are c…
Bayesian Optimization Allowing for Common Random Numbers
Michael Pearce, Matthias Poloczek, Juergen Branke
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning. M…
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob R Gardner +2
Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional…
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista, Matthias Poloczek
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The com…
Comparing the Finite-Time Performance of Simulation-Optimization Algorithms
Naijia Dong, David J. Eckman, Matthias Poloczek +2
We empirically evaluate the finite-time performance of several simulation-optimization algorithms on a testbed of problems with the goal of motivating further development of algori…
Greedy Matching: Guarantees and Limitations
Bert Besser, Matthias Poloczek
Since Tinhofer proposed the MinGreedy algorithm for maximum cardinality matching in 1984, several experimental studies found the randomized algorithm to perform excellently for var…