13 citations · 20 across the 4 of their papers we have counts for
6 papers
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…
Active Preference Learning for Personalized Portfolio Construction
Kevin Tee, Michael McCourt, Ruben Martinez-Cantin +2
In financial asset management, choosing a portfolio requires balancing returns, risk, exposure, liquidity, volatility and other factors. These concerns are difficult to compare exp…
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…
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…