PrefStat is a permanent inter-university research and working group that aims at disseminating statistical approaches in Preference Learning, understood as modeling and analysis of preference rankings and ordinal data related to preference analysis. PrefStat organizes specialized sessions at international conferences, workshops, and a summer school every year in a different location.
PrefStat 2025: Second International Summer School on Preference Learning for Ranking and Ordinal Data
30th June – 4th July, 2025
University of Oslo, Norway
PrefStat organizes a series of summer schools designed to provide a comprehensive overview of preference statistics, a rapidly growing field that has gained significant attention in recent years due to the numerous application fields involving human preferences (from recommender systems to Large Language Models, from surveys and psychological experiments to marketing, economics, and political science). PrefStat thus establishes a series of high-level courses on cutting-edge topics in the specific context of Statistical Learning from Preference Information, or Preference Learning. Preference learning is concerned with all data analyses involving preferences, rankings, ratings, clicking, or any kind of ordinal data. It entails modeling experiments involving a set of assessors (experts, judges, users) who express order relations about a set of items, thus being a subfield of both supervised and unsupervised statistical learning.
The school will provide a deep introduction to the topic and insight into more challenging tasks that are of interest in modern applications, such as handling partial, unstructured, exogenous information, individual preference prediction, and importance feature selection. PrefStat 2025 will combine lectures delivered by internationally leading scholars on the specific designated topic and supervised practical tutorials.
The course is structured around three main objectives:
- Introduction to preference statistics: We will introduce the fundamental concepts and techniques of preference learning, including the different types of preference data, the measures of agreement and disagreement, and the methods for analyzing preference data.
- Advanced methods in Bayesian Preference Learning: We will delve deeper into the analysis of preference data, by introducing methods in probabilistic (Bayesian) preference learning, including score-based and distance-based methods, and methods for Partial Orders.
- Computational methods for preference statistics: We will discuss the implementation of preference learning methods through computational tools with R.
Target group: PhD students and postdocs in the area of data science, machine learning, statistics, and related fields. Master students with basic skills in machine learning and statistics are also welcome. We aim to provide participants with a solid foundation in preference learning, and to equip them with the skills and knowledge necessary to apply these methods to real-world problems.
We look forward to seeing you at the summer school!
Lecturers:
Marta Crispino, Bank of Italy, IT
Luca Coraggio, University of Naples Federico II, IT
Antonio D’Ambrosio, University of Naples Federico II, IT
Anne-Marie George, University Of Oslo, NO
Kate Lee, University of Auckland, NZ
Cristina Mollica, University of Rome “Sapienza”, IT
Geoff Nicholls, University of Oxford, UK
Maurizio Romano, University of Cagliari, IT
Øystein Sørensen, University of Oslo, NO
Luca Tardella, University of Rome “Sapienza”, IT
Registration fees will cover the lectures, materials, gadgets, lunches, and social events.
Information on registration will be updated on this page in early 2025.
Please note that the summer school facilities allow only a limited number of participants. Please make contact at info at prefstat dot org if interested!