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arXiv · 1702.02849

Coordinated Online Learning With Applications to Learning User Preferences

Christoph Hirnschall·Adish Singla·Sebastian Tschiatschek·Andreas Krause
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Abstract

We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners. We consider an algorithmic framework to model the relationship of these tasks via a set of convex constraints. To exploit this relationship, we design a novel algorithm -- COOL -- for coordinating the individual online learners: Our key idea is to coordinate their parameters via weighted projections onto a convex set. By adjusting the rate and accuracy of the projection, the COOL algorithm allows for a trade-off between the benefit of coordination and the required computation/communication. We derive regret bounds for our approach and analyze how they are influenced by these trade-off factors. We apply our results on the application of learning users' preferences on the Airbnb marketplace with the goal of incentivizing users to explore under-reviewed apartments.

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Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause. 2017-02-09. Coordinated Online Learning With Applications to Learning User Preferences. https://arxiv.org/abs/1702.02849

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