Better Algorithms for Selective Sampling

We study online algorithms for selective sampling that use regularized least squares (RLS) as base classifier. These algorithms typically perform well in practice, and some of them have formal guarantees on their mistake and query rates. We refine and extend these guarantees in various ways, proposing algorithmic variants that exhibit better empirical behavior while enjoying performance guarantees under much more general conditions. We also show a simple way of coupling a generic gradient-based classifier with a specific RLSbased selective sampler, obtaining hybrid algorithms with combined performance guarantees.