<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Preference Learning on Øystein Sørensen</title><link>https://osorensen.no/tags/preference-learning/</link><description>Recent content in Preference Learning on Øystein Sørensen</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 28 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://osorensen.no/tags/preference-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>BayesMallowsSMC2: Nested Sequential Monte Carlo for the Bayesian Mallows Model</title><link>https://osorensen.no/software/software6/</link><pubDate>Fri, 08 May 2026 00:00:00 +0000</pubDate><guid>https://osorensen.no/software/software6/</guid><description>This R package provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model.</description></item><item><title>Sequential Rank and Preference Learning with the Bayesian Mallows Model</title><link>https://osorensen.no/papers/paper11/</link><pubDate>Sun, 01 Dec 2024 00:00:00 +0000</pubDate><guid>https://osorensen.no/papers/paper11/</guid><description>This paper considers rank and preference modeling for the case in which data arrive sequentially, rather than in a batch. The goal is to compute the posterior distribution incrementally in time, so that it can be quickly updated when new data arrives. To this end, we develop a sequential Monte Carlo algorithm for the Bayesian Mallows model. Published in Bayesian Analysis.</description></item><item><title>Probabilistic preference learning with the Mallows rank model</title><link>https://osorensen.no/papers/paper3/</link><pubDate>Sun, 01 Apr 2018 00:00:00 +0000</pubDate><guid>https://osorensen.no/papers/paper3/</guid><description>This paper studies the analysis of rank and preference data. We consider both complete rankings, partial rankings, and pairwise preferences. We develop a complete Bayesian framework for estimating Mallows&amp;rsquo; rank model in all this cases, including clustering of users with similar preferences and preference prediction. Published in Journal of Machine Learning Research. Joint first authorship with Valeria Vitelli.</description></item></channel></rss>