Kendall embedding of a permutation.
Abstract
We show that the widely used Kendall tau correlation coefficient is a positive definite kernel for permutations. It offers a computationally attractive alternative to more complex kernels on the symmetric group to learn from rankings, or to learn to rank. We show how to extend it to partial rankings or rankings with uncertainty, and demonstrate promising results on high-dimensional classification problems in biomedical applications.
Type
Publication
In Proceedings of the 32nd International Conference on Machine Learning (ICML)
Yunlong Jiao
Applied Machine Learning Research
My research interests include Deep Generative Models, Vision Language Models, Natural Language Processing, and Computational Biology.