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Normalized Online Learning

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arxiv 1305.6646 v1 pith:QZH6YHH4 submitted 2013-05-28 cs.LG stat.ML

Stephane Ross , Paul Mineiro , John Langford This is my paper · ORCID
classification cs.LGstat.ML
keywords algorithmsdatalearningonlinescalesabsoluteboundscomplexity
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We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale. This has several useful effects: there is no need to pre-normalize data, the test-time and test-space complexity are reduced, and the algorithms are more robust.

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  1. Accelerated learning from recommender systems using multi-armed bandit

    cs.IR 2019-08 conditional novelty 4.0 of 10

    A Vrbo team used daily Thompson sampling to rank four recommendation models by click-through rate, but the A/B validation they report is for a previous campaign's winner, not the current one.

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