Neural Active Learning with Performance Guarantees
- URL: http://arxiv.org/abs/2106.03243v1
- Date: Sun, 6 Jun 2021 20:44:23 GMT
- Title: Neural Active Learning with Performance Guarantees
- Authors: Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari,
Zhilei Wang
- Abstract summary: We investigate the problem of active learning in the streaming setting in non-parametric regimes, where the labels are 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.
- Score: 37.16062387461106
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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