3 citations · 5 across the 2 of their papers we have counts for
3 papers
Pseudo-Mallows for Efficient Probabilistic Preference Learning
Qinghua Liu, Valeria Vitelli, Carlo Mannino +2
We propose the Pseudo-Mallows distribution over the set of all permutations of items, to approximate the posterior distribution with a Mallows likelihood. The Mallows model has…
Time-to-Event Prediction with Neural Networks and Cox Regression
Håvard Kvamme, Ørnulf Borgan, Ida Scheel
New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks. Building on methodology from nested case-control studies…
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Qinghua Liu, Andrew Henry Reiner, Arnoldo Frigessi +1
Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Curre…