Meta-strategy for Learning Tuning Parameters with Guarantees
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Online gradient methods, like the online gradient algorithm (OGA), often depend on tuning parameters that are difficult to set in practice. We consider an online meta-learning scenario, and we propose a meta-strategy to learn these parameters from past tasks. Our strategy is based on the minimization of a regret bound. It allows to learn the initialization and the step size in OGA with guarantees. We provide a regret analysis of the strategy in the case of convex losses. It suggests that, when there are parameters θ_1,…,θ_T solving well tasks 1,…,T respectively and that are close enough one to each other, our strategy indeed improves on learning each task in isolation.
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