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This work addresses various open questions in the theory of active learningfor nonparametric classification. Our contributions are both statistical andalgorithmic: -We establish new minimax-rates for active learning under common\textit{noise conditions}. These rates display interesting transitions -- dueto the interaction between noise \textit{smoothness and margin} -- not presentin the passive setting. Some such transitions were previously conjectured, butremained unconfirmed. -We present a generic algorithmic strategy for adaptivityto unknown noise smoothness and margin; our strategy achieves optimal rates inmany general situations; furthermore, unlike in previous work, we avoid theneed for \textit{adaptive confidence sets}, resulting in strictly milderdistributional requirements.

Adaptivity to Noise Parameters in Nonparametric Active Learning