HIGHLIGHTS
- who: M. Lomelí from the Computational and Biological Learning Lab, University of Cambridge, Cambridge, UK have published the research: Antithetic and Monte Carlo kernel estimators for partial rankings, in the Journal: (JOURNAL)
- what: The corresponding estimator has lower variance and the authors demonstrate empirically that it has a better performance in a variety of machine learning tasks. The authors provide an overview of the connection between kernels and certain semimetrics when working on the space of permutations. The authors propose a variety of Monte Carlo methods to estimate the marginalised kernel of Eq for the . . .
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