  Real-world engineering systems are typically compared and contrasted using
multiple metrics. For practical machine learning systems, performance tuning is
often more nuanced than minimizing a single expected loss objective, and it may
be more realistically discussed as a multi-objective optimization problem. We
propose a novel generative model for scalar-valued utility functions to capture
human preferences in a multi-objective optimization setting. We also outline an
interactive active learning system that sequentially refines the understanding
of stakeholders ideal utility functions using binary preference queries.
