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A tutorial on learning from preferences and choices with Gaussian Processes
March 19, 2024, 4:42 a.m. | Alessio Benavoli, Dario Azzimonti
cs.LG updates on arXiv.org arxiv.org
Abstract: Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics. By understanding individuals' preferences and how they make choices, we can build products that closely match their expectations, paving the way for more efficient and personalised applications across a wide range of domains. The objective of this tutorial is to present a cohesive and comprehensive framework for preference learning with Gaussian Processes (GPs), demonstrating how to seamlessly incorporate rationality principles (from …
abstract applications arxiv build cs.lg decision economics gaussian processes intersection lies machine machine learning match modelling personalised processes products statistics stat.ml theory the way tutorial type understanding
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