Fast and Accurate Inference of Plackett-Luce Models

By Lucas Maystre, Matthias Grossglauser. 2015

141 citations recorded by the source corpus.

Subfields: Statistics and Probability, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Venkatesh Saligrama
2. Alexander Olshevsky
3. Julien M. Hendrickx
4. Ashish Khetan
5. Naoki Masuda
6. Renaud Lambiotte
7. M. A. Porter
8. Sewoong Oh

In Probabilistic & Bayesian Methods

1. Naoki Masuda
2. Venkatesh Saligrama
3. Alexander Olshevsky
4. Renaud Lambiotte
5. M. A. Porter
6. Sewoong Oh

Citations in the corpus

• Minimax Rate for Learning From Pairwise Comparisons in the BTL Model (2020, background): “…are many variations of the problem that have been studied, from more sophisticated models such as Thurstone and Placket-Luce (Hajek et al. , 2014; Maystre & Grossglauser, 2015), to online or bandit versions (Sz¨or´enyi et al. , 2015; Yue et al. , 2012), to models with active learning (Jamieson &…”
• Random walks and diffusion on networks (2016, methodology): “Taking advantage of this relationship between the CTRW and the maximum-likelihood estimator of the multinomial logit model has resulted in inference algorithms for the multinomial logit model that is faster and more accurate than previous methods for several data sets [330].”
• Computational and Statistical Tradeoffs in Learning to Rank (2016, background): “This is one of the motivations behind the algorithmic advances in learning from pairwise comparisons [19, 21, 17].”