Antithetic and Monte Carlo kernel estimators for partial rankings

In the modern age, rankings data are ubiquitous and they are useful for a variety of applications such as recommender systems, multi-object tracking and preference learning. However, most rankings data encountered in the real world are incomplete, which prevent the direct application of existing mod...

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Veröffentlicht in:	Statistics and computing 2019-09, Vol.29 (5), p.1127-1147
Hauptverfasser:	Lomelí, M., Rowland, M., Gretton, A., Ghahramani, Z.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Computer simulation Estimators Kernels Machine learning Mathematics and Statistics Monte Carlo simulation Permutations Probability and Statistics in Computer Science Recommender systems Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs
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