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We propose a method to learn a common bias vector for a growing sequence oflow-variance tasks. Unlike state-of-the-art approaches, our method does notrequire tuning any hyper-parameter. Our approach is presented in thenon-statistical setting and can be of two variants. The "aggressive" oneupdates the bias after each datapoint, the "lazy" one updates the bias only atthe end of each task. We derive an across-tasks regret bound for the method.When compared to state-of-the-art approaches, the aggressive variant returnsfaster rates, the lazy one recovers standard rates, but with no need of tuninghyper-parameters. We then adapt the methods to the statistical setting: theaggressive variant becomes a multi-task learning method, the lazy one ameta-learning method. Experiments confirm the effectiveness of our methods inpractice.

Online Parameter-Free Learning of Multiple Low Variance Tasks