5 citations · 6 across the 3 of their papers we have counts for
5 papers
Sampling Humans for Optimizing Preferences in Coloring Artwork
Michael McCourt, Ian Dewancker
Many circumstances of practical importance have performance or success metrics which exist implicitly---in the eye of the beholder, so to speak. Tuning aspects of such problems req…
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…
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…
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…