We investigate the problem of active learning in the streaming setting in
non-parametric regimes, where the labels are stochastically generated from a
class of functions on which we make no assumptions whatsoever. We rely on
recently proposed Neural Tangent Kernel (NTK) approximation tools to construct
a suitable neural embedding that determines the feature space the algorithm
operates on and the learned model computed atop. Since the shape of the label
requesting threshold is tightly related to the complexity of the function to be
learned, which is a-priori unknown, we also derive a version of the algorithm
which is agnostic to any prior knowledge. This algorithm relies on a regret
balancing scheme to solve the resulting online model selection problem, and is
computationally efficient. We prove joint guarantees on the cumulative regret
and number of requested labels which depend on the complexity of the labeling
function at hand. In the linear case, these guarantees recover known minimax
results of the generalization error as a function of the label complexity in a
standard statistical learning setting.

研究了非参数制度下流式学习中的主动学习问题，使用最近提出的神经切向核近似工具构建适当的神经嵌入来确定算法操作的特征空间和计算在其上的学习模型。提出了一种对先前知识不可知的版本算法，并通过遗憾平衡方案解决了所得到的在线模型选择问题，该算法具有计算效率，提供了关于累积遗憾和请求标签数量的联合保证，这些保证取决于手头标记函数的复杂度。在线性情况下，这些保证恢复了关于标记复杂度的泛化误差的已知的极小值结果。