A-Optimal Active Learning
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Abstract (arXiv)
In this work we discuss the problem of active learning. We present an approach that is based on A-optimal experimental design of ill-posed problems and show how one can optimally label a data set by partially probing it, and use it to train a deep network. We present two approaches that make different assumptions on the data set. The first is based on a Bayesian interpretation of the semi-supervised learning problem with the graph Laplacian that is used for the prior distribution and the second is based on a frequentist approach, that updates the estimation of the bias term based on the recovery of the labels. We demonstrate that this approach can be highly efficient for estimating labels and training a deep network.
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References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
- 2017Cost-Effective Active Learning for Deep Image Classificationcited by 165
- 2018Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithmscited by 22
- 2015S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classificationcited by 20
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