67 citations · 164 across the 4 of their papers we have counts for
4 papers
Bayesian Optimization for Machine Learning : A Practical Guidebook
Ian Dewancker, Michael McCourt, Scott Clark
The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices. It is our hope that t…
Evaluation System for a Bayesian Optimization Service
Ian Dewancker, Michael McCourt, Scott Clark +3
Bayesian optimization is an elegant solution to the hyperparameter optimization problem in machine learning. Building a reliable and robust Bayesian optimization service requires c…
A Stratified Analysis of Bayesian Optimization Methods
Ian Dewancker, Michael McCourt, Scott Clark +3
Empirical analysis serves as an important complement to theoretical analysis for studying practical Bayesian optimization. Often empirical insights expose strengths and weaknesses…
Parallel Bayesian Global Optimization of Expensive Functions
Jialei Wang, Scott C. Clark, Eric Liu +1
We consider parallel global optimization of derivative-free expensive-to-evaluate functions, and propose an efficient method based on stochastic approximation for implementing a co…