  We improve the theoretical and empirical performance of
neural-network(NN)-based active learning algorithms for the non-parametric
streaming setting. In particular, we introduce two regret metrics by minimizing
the population loss that are more suitable in active learning than the one used
in state-of-the-art (SOTA) related work. Then, the proposed algorithm leverages
the powerful representation of NNs for both exploitation and exploration, has
the query decision-maker tailored for $k$-class classification problems with
the performance guarantee, utilizes the full feedback, and updates parameters
in a more practical and efficient manner. These careful designs lead to an
instance-dependent regret upper bound, roughly improving by a multiplicative
factor $O(\log T)$ and removing the curse of input dimensionality. Furthermore,
we show that the algorithm can achieve the same performance as the
Bayes-optimal classifier in the long run under the hard-margin setting in
classification problems. In the end, we use extensive experiments to evaluate
the proposed algorithm and SOTA baselines, to show the improved empirical
performance.
