
We propose two novel algorithms, NEURONAL-S and NEURONAL-P, for stream-based and pool-based active learning that mitigate the adverse impacts of the number of classes (K) on the performance and computational costs of existing bandit-based approaches. Our methods achieve improved theoretical guarantees and empirical performance.


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neural-active-learning-with-efficient-exploitation-and-exploration-networks


Neural Active Learning with Efficient Exploitation and Exploration Networks
