26 citations · 36 across the 3 of their papers we have counts for
3 papers
cs.DC2018★ 2 cited
Orchestrate: Infrastructure for Enabling Parallelism during Hyperparameter Optimization
Alexandra Johnson, Michael McCourt
Two key factors dominate the development of effective production grade machine learning models. First, it requires a local software implementation and iteration process. Second, it…
cs.LG2016★ 8 cited
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…
cs.LG2016★ 26 cited
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…