학술논문

Sequential Preference-Based Optimization
Document Type
Working Paper
Source
Subject
Computer Science - Learning
Computer Science - Computational Engineering, Finance, and Science
Computer Science - Human-Computer Interaction
Statistics - Machine Learning
Language
Abstract
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 optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow for observations of equivalent preference from users.