In many real-world applications, customers express their preferences via direct choices. Imagine we are interested in proposing the best gaming platform to a customer. We could survey users for a ranking of the available platforms and then learn the relationship between a customer's features and their preferences. In this talk we will review the fundamental properties of preference theory and its association with utility functions. We will show that Gaussian processes (GPs) are effective tools for learning preferences. By adjusting the likelihood, we can adapt to different types of preferential data: from pairwise preferences to general choices among items within a finite set. Finally, we will highlight some applications of these methods to Bayesian optimization tasks.
Speaker:
Dario Azzimonti (IDSIA). Personal website can be found at https://sites.google.com/view/darioazzimonti/.
This talk was given at Secondmind Labs, as a part of our (virtual) research seminar. Our research seminar is where we exchange ideas with guest speakers, keeping you up to date with the latest developments and inspiring research topics. Occasionally, Secondmind researchers present their own work as well. You can find a complete list of speakers at https://www.secondmind.ai/research/labs/seminars/. Learn more about Secondmind Labs at https://www.secondmind.ai/research/labs/
