Summary of A Tutorial on Learning From Preferences and Choices with Gaussian Processes, by Alessio Benavoli and Dario Azzimonti
A tutorial on learning from preferences and choices with Gaussian Processes
by Alessio Benavoli, Dario Azzimonti
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning tutorial presents a comprehensive framework for preference learning using Gaussian Processes (GPs), incorporating rationality principles from economics and decision theory. The framework enables the construction of preference learning models that encompass random utility models, limits of discernment, and scenarios with multiple conflicting utilities. This approach can be applied to various domains, building products that closely match individuals’ expectations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to use machine learning to understand people’s preferences and make better choices. By combining ideas from economics, decision-making, and statistics, the authors create a new way to learn about people’s preferences using Gaussian Processes (GPs). This method helps build products that are tailored to what people want, making it more efficient and personalized. |
Keywords
* Artificial intelligence * Machine learning
