Please visit the new dedicated website: prefstat.org

PREFerence STATistics
First Summer School in Advanced Statistical Learning
for Preference, Ranking, and Ordinal Data

2-6 September, 2024
Museo di Arte Contemporanea (Calasetta (SU), Sardinia, Italy)

Calasetta_day This summer school is designed to provide a comprehensive overview of preference statistics, a rapidly growing field that has gained significant attention in recent years. Therefore, the aim of PrefStat is to establish a series of high-level courses on cutting-edge topics in Statistics and Machine Learning in the specific context of Statistical Learning for Preference information.

Preference statistics is concerned with all data analyses involving preferences, rankings, ratings, clicking, or any kind of ordinal data. It has applications in various fields, including marketing, psychology, economics, and political science, whilst being a subfield of both supervised and unsupervised statistical learning, which entails modeling experiments involving a set of assessors (experts, judges, users) who express order relations about a set of items.
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. The Summer School 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

  1. Introduction to preference statistics: We will introduce the fundamental concepts and techniques of preference statistics, including the different types of preference data, the measures of agreement and disagreement, and the methods for analyzing preference data;
  2. Advanced ranking and ordinal data: We will delve deeper into the analysis of ranking and ordinal data, including the use of distance-based models, probabilistic preference learning, and other advanced techniques;
  3. Computational methods for preference statistics: We will discuss the implementation of preference statistical methods through computational tools with R.

We aim to provide you with a solid foundation in preference statistics and equip you with
the skills and knowledge necessary to apply these methods to real-world problems.
We look forward to seeing you at the summer school!

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.

Lecturers:

Brendan Murphy, University College Dublin, IE
Antonio D’Ambrosio, University of Naples Federico II, IT
Valeria Vitelli, University of Oslo, NO
Antonella Plaia, University of Palermo, IT
Mariangela Sciandra, University of Palermo, IT
Alessandro Albano, University of Palermo, IT
Cristina Mollica, University of Rome “Sapienza”, IT
Maurizio Romano, University of Cagliari, IT
Claudio Conversano, University of Cagliari, IT

This summer school will take place in Calasetta, a beautiful small village in Sardinia (Italy), in the Museum of Contemporary Arts.

Calasetta_afternoon

Registration fees: they cover the lectures, materials, gadgets,
lunches (or not), and social events.
Information and registration on this page
Please note that the conference room allows only a very limited number of participants.