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Fast and Accurate Inference of Plackett–Luce Models
Lucas Maystre · Matthias Grossglauser

Mon Dec 07 04:00 PM -- 08:59 PM (PST) @ 210 C #49 #None

We show that the maximum-likelihood (ML) estimate of models derived from Luce's choice axiom (e.g., the Plackett-Luce model) can be expressed as the stationary distribution of a Markov chain. This conveys insight into several recently proposed spectral inference algorithms. We take advantage of this perspective and formulate a new spectral algorithm that is significantly more accurate than previous ones for the Plackett--Luce model. With a simple adaptation, this algorithm can be used iteratively, producing a sequence of estimates that converges to the ML estimate. The ML version runs faster than competing approaches on a benchmark of five datasets. Our algorithms are easy to implement, making them relevant for practitioners at large.

Author Information

Lucas Maystre (EPFL)
Matt Grossglauser (EPFL)

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