TopRank+: A Refinement of TopRank Algorithm
Machine Learning
2020-01-22 v1 Machine Learning
Abstract
Online learning to rank is a core problem in machine learning. In Lattimore et al. (2018), a novel online learning algorithm was proposed based on topological sorting. In the paper they provided a set of self-normalized inequalities (a) in the algorithm as a criterion in iterations and (b) to provide an upper bound for cumulative regret, which is a measure of algorithm performance. In this work, we utilized method of mixtures and asymptotic expansions of certain implicit function to provide a tighter, iterated-log-like boundary for the inequalities, and as a consequence improve both the algorithm itself as well as its performance estimation.
Cite
@article{arxiv.2001.07617,
title = {TopRank+: A Refinement of TopRank Algorithm},
author = {Victor de la Pena and Haolin Zou},
journal= {arXiv preprint arXiv:2001.07617},
year = {2020}
}