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The Ethereal
Scale-Free Algorithms for Online Linear Optimization
February 19, 2015 ยท Declared Dead ยท ๐ International Conference on Algorithmic Learning Theory
"No code URL or promise found in abstract"
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Authors
Francesco Orabona, David Pal
arXiv ID
1502.05744
Category
cs.LG: Machine Learning
Cross-listed
math.OC
Citations
55
Venue
International Conference on Algorithmic Learning Theory
Last Checked
5 months ago
Abstract
We design algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors. We achieve adaptiveness to norms of loss vectors by scale invariance, i.e., our algorithms make exactly the same decisions if the sequence of loss vectors is multiplied by any positive constant. Our algorithms work for any decision set, bounded or unbounded. For unbounded decisions sets, these are the first truly adaptive algorithms for online linear optimization.
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