67 citations · 115 across the 8 of their papers we have counts for
8 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…
Interactive Preference Learning of Utility Functions for Multi-Objective Optimization
Ian Dewancker, Michael McCourt, Samuel Ainsworth
Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than m…
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…
Preemptive Termination of Suggestions during Sequential Kriging Optimization of a Brain Activity Reconstruction Simulation
Michael McCourt, Ian Dewancker, Salvatore Ganci
Reconstructing brain activity through electroencephalography requires a boundary value problem (BVP) solver to take a proposed distribution of current dipoles within the brain and…
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…