title: Online Active Learning with Surrogate Loss Functions
abstract: We derive a novel active learning algorithm in the streaming setting for binary classification tasks. The algorithm leverages 
  weak labels to minimize the number of label requests, and trains a model to optimize a surrogate loss on a resulting set of labeled and 
  weak-labeled points. Our algorithm jointly admits two crucial properties: theoretical guarantees in the general agnostic setting and a strong empirical performance. Our theoretical analysis shows that the algorithm attains favorable generalization and label complexity bounds, while our empirical study on 18 real-world datasets demonstrate that the algorithm outperforms standard baselines, including the Margin Algorithm, or  Uncertainty Sampling, a high-performing active learning algorithm favored by practitioners.
authors: Giulia DeSalvo Claudio Gentile Tobias Sommer Thune
hIndex: Claudio Gentile 40