Abstract
We consider a ranking regression problem in which we use a dataset of ranked choices to learn Plackett-Luce scores as functions of sample features. We solve the maximum likelihood estimation problem by using the Alternating Directions Method of Multipliers (ADMM), effectively separating the learning of scores and model parameters. This separation allows us to express scores as the stationary distribution of a continuous-time Markov Chain. Using this equivalence, we propose two spectral algorithms for ranking regression that learn model parameters up to 579 times faster than the Newton's method.
Original language | English (US) |
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Pages (from-to) | 77-88 |
Number of pages | 12 |
Journal | Proceedings of Machine Learning Research |
Volume | 108 |
State | Published - 2020 |
Event | 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 - Virtual, Online Duration: Aug 26 2020 → Aug 28 2020 |
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability