TopRank+: A Refinement of TopRank Algorithm

• URL: http://arxiv.org/abs/2001.07617v1
• Date: Tue, 21 Jan 2020 15:44:44 GMT
• Title: TopRank+: A Refinement of TopRank Algorithm
• Authors: Victor de la Pena, Haolin Zou
• Abstract summary: A novel online learning algorithm was proposed based on topological sorting. In this work, we utilize method of mixtures and expansions of certain implicit function to provide a tighter, iterated-log-like boundary for the inequalities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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