Animashree Anandkumar

Active Learning under Label Shift

2021 · CaltechAUTHORS (California Institute of Technology)

We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a tradeoff between importance weighting and class-balanced sampling …

Deep Active Learning for Named Entity Recognition

2017

Deep neural networks have advanced the state of the art in named entity recognition. However, under typical training procedures, advantages over classical methods emerge only with large datasets. As a result, deep learning is employed …