Online Learning with Abstention


Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1067-1075, 2018.


We present an extensive study of a key problem in online learning where the learner can opt to abstain from making a prediction, at a certain cost. In the adversarial setting, we show how existing online algorithms and guarantees can be adapted to this problem. In the stochastic setting, we first point out a bias problem that limits the straightforward extension of algorithms such as UCB-N to this context. Next, we give a new algorithm, UCB-GT, that exploits historical data and time-varying feedback graphs. We show that this algorithm benefits from more favorable regret guarantees than a natural extension of UCB-N . We further report the results of a series of experiments demonstrating that UCB-GT largely outperforms that extension of UCB-N, as well as other standard baselines.

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