Wouter Koolen | MetaGrad: Online Convex Optimization in Individual-Sequence and Stochastic Settings

This talk will be about MetaGrad, a new algorithm for Online Convex Optimization.
  • When Jun 16, 2016 from 11:00 AM to 01:00 PM (Europe/Amsterdam / UTC200)
  • Where L236
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We will start by reviewing the Online Convex Optimization problem, the Online Gradient Descent strategy and its performance guarantees. We will then look at the design of a new algorithm, called MetaGrad, which is based on the aggregation of multiple learning rates. We will show that MetaGrad has individual-sequence performance guarantees that express that it may often outperform the worst-case lower bound. And we will conclude by showing that in stochastic settings these regret guarantees lead to adaptivity to a certain friendliness parameter (the Bernstein exponent) of the generating distribution.

MetaGrad paper: http://arxiv.org/abs/arXiv:1604.08740
Stochastic fast rates paper: http://arxiv.org/abs/arXiv:1605.06439