ActiveLabeling: StreamingStochasticGradients

The workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Interestingly, it appears that one does not need full supervision to access stochastic gradients, which is the main motivation of this paper. After formalizing the"activelabeling" problem, whichfocuses onactivelearningwith partial supervision, we provide a streaming technique that provably minimizes the ratio of generalization error over the number of samples.