Monday June 30^th 2025
Lecturers: Luca Coraggio, Antonio D’Ambrosio, Maurizio Romano
Introduction to Preference Learning
9:00 – 10:30 Basic preference learning; Introduction to rank data; data visualization; descriptives; distances for rankings
10:30 – 11:00 Coffee break
11:00 – 12:30 LAB: Use or R packages to handling rank data: data visualization; descriptives; distances
12:30 – 13:30 Lunch
13:30 – 15:00 Rank aggregation problem, heuristics, introduction to supervised and unsupervised preference learning
15:00 – 15:30 Coffee break
15:30 – 17:00 LAB: Rank aggregation problem, examples of supervised and unsupervised preference learning
Evening (17:30 – 19:00): Welcome Reception

Tuesday July 1^st 2025
Lecturers: Marta Crispino, Anne-Marie George, Øystein Sørensen
Distance-based models for Preference Learning
9:00 – 10:30 The Mallows model, the normalizing constant, ML inference (basic)
10:30 – 11:00 Coffee break
11:00 – 12:30 LAB: The Mallows model, the normalizing constant, ML inference with R and python. Examples from social choice theory
12:30 – 13:30 Lunch
13:30 – 15:00 Bayesian inference in the Mallows model. Algorithms
15:00 – 15:30 Coffee break
15:30 – 17:00 LAB: Bayesian inference in the Mallows model with the BayesMallows package

Wednesday July 2^nd 2025
Lecturers: Cristina Mollica & Luca Tardella
Bayesian Preference Learning via the Plackett-Luce (part I)
9:00 – 10:30 Stagewise models in the ranking literature; A focus on the Plackett-Luce model (PL); Extensions of the basic PL
10:30 – 11:00 Coffee break
11:00 – 12:30 LAB: The R package PLMIX; Practical examples with R
12:30 – 13:30 Lunch
Afternoon (13:30 – 16:00): SURPRISE!!
Evening (18:00 – 20:00): Social Excursion at the Oslo Botanical Garden

Thursday July 3^rd 2025
Lecturers: Cristina Mollica & Luca Tardella
Bayesian Preference Learning via the Plackett-Luce (part II)
9:00 – 10:30 Finite PL mixtures: from the MLE to Bayesian inference; Bayesian model comparison criteria; Bayesian model assessment; Handling of label-switching
10:30 – 11:00 Coffee break
11:00 – 12:30 LAB: The R package PLMIX; Practical examples with R
12:30 – 13:30 Lunch
Lecturers: Kate Lee & Geoff Nicholls
Bayesian Inference for Partial Orders from Rank-Order Data (part I)
13:30 – 15:00 What is a Partial Order? Key properties (depth, dimension); Why order partially? Social hierarchies and other applications; Rank-Order data as random linear extensions of a Partial Order; Sequential observation models for Partial Orders. Queue-jumping. Priors for Partial Orders: the bad and the good
15:00 – 15:30 Coffee break
15:30 – 17:00 LAB: Explore basic Partial Order properties in R
Evening (18:00 – 20:00): Social Dinner at the wonderful Brasserie Opera

Friday July 4^th 2025
Lecturers: Kate Lee & Geoff Nicholls
Bayesian Inference for Partial Orders from Rank-Order Data (part II)
9:00 – 10:30 The Posterior Distribution. Basic MCMC methods; Quantifying Uncertainty in Partial Order reconstructions; The Royal Acta data and witness lists. Software overview (R code for fitting Partial Orders)
10:30 – 11:00 Coffee break
11:00 – 12:30 LAB: Fit Partial  Order models using R. Fixed time and time-series (Python code may also be available for those who prefer/if desired)
12:30 – 13:30 Lunch & closing