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Poster
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Tor Lattimore

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #33
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Existing strategies for finite-armed stochastic bandits mostly depend on a parameter of scale that must be known in advance. Sometimes this is in the form of a bound on the payoffs, or the knowledge of a variance or subgaussian parameter. The notable exceptions are the analysis of Gaussian bandits with unknown mean and variance by Cowan and Katehakis [2015a] and of uniform distributions with unknown support [Cowan and Katehakis, 2015b]. The results derived in these specialised cases are generalised here to the non-parametric setup, where the learner knows only a bound on the kurtosis of the noise, which is a scale free measure of the extremity of outliers.

Keywords: Bandit Algorithms · Online Learning
[ Paper] 

Author Information

Tor Lattimore (DeepMind)

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