# Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

arXiv: https://arxiv.org/abs/1612.04453 · HTML: https://mlsanity.com/paper/1612.04453/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2016.

## Built on by (most cited first, 2 of 2)

- [Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes](https://mlsanity.com/paper/2203.11382/) (2022)
- [The Efficacy of Utility Functions for Multicriteria Hospital Case-Mix Planning](https://mlsanity.com/paper/2308.07321/) (2023)

## Abstract (arXiv)

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.