A regularity tangent vector, computable with constant training overhead, turns the loss gradient at a candidate point into an estimate of its influence on model complexity for active learning.
Second-Order Stochastic Optimization for Machine Learning in Linear Time, 2017
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Influence functions and regularity tangents for efficient active learning
A regularity tangent vector, computable with constant training overhead, turns the loss gradient at a candidate point into an estimate of its influence on model complexity for active learning.