Bounds on the Generalization Error in Active Learning
This work provides theoretical foundations for principled construction and evaluation of query algorithms in active learning, addressing a core challenge in machine learning.
The paper tackles the problem of bounding generalization error in active learning by deriving upper bounds based on empirical risk minimization principles, showing that combining informativeness and representativeness query strategies yields superior algorithms, with regularization techniques ensuring bound validity.
We establish empirical risk minimization principles for active learning by deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the bounds suggest that superior query algorithms can be obtained by combining both informativeness and representativeness query strategies, where the latter is assessed using integral probability metrics. To facilitate the use of these bounds in application, we systematically link diverse active learning scenarios, characterized by their loss functions and hypothesis classes to their corresponding upper bounds. Our results show that regularization techniques used to constraint the complexity of various hypothesis classes are sufficient conditions to ensure the validity of the bounds. The present work enables principled construction and empirical quality-evaluation of query algorithms in active learning.