13 citations · 23 across the 6 of their papers we have counts for
7 papers · 1 filter
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Ryan Turner, David Eriksson, Michael McCourt +4
This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importa…
Efficient Rollout Strategies for Bayesian Optimization
Eric Hans Lee, David Eriksson, Bolong Cheng +2
Bayesian optimization (BO) is a class of sample-efficient global optimization methods, where a probabilistic model conditioned on previous observations is used to determine future…
Sequential Preference-Based Optimization
Ian Dewancker, Jakob Bauer, Michael McCourt
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimizatio…
Practical Bayesian optimization in the presence of outliers
Ruben Martinez-Cantin, Kevin Tee, Michael McCourt
Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is…
Robust Bayesian Optimization with Student-t Likelihood
Ruben Martinez-Cantin, Michael McCourt, Kevin Tee
Bayesian optimization has recently attracted the attention of the automatic machine learning community for its excellent results in hyperparameter tuning. BO is characterized by th…
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…