We study the problem of interactively learning a binary classifier using
noisy labeling and pairwise comparison oracles, where the comparison oracle
answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct
labels is harder but pairwise comparisons are easier, and the algorithm can
leverage both types of oracles...
In this paper, we attempt to characterize how
the access to an easier comparison oracle helps in improving the label and
total query complexity. We show that the comparison oracle reduces the learning
problem to that of learning a threshold function. We then present an algorithm
that interactively queries the label and comparison oracles and we characterize
its query complexity under Tsybakov and adversarial noise conditions for the
comparison and labeling oracles. Our lower bounds show that our label and total
query complexity is almost optimal.