Active Learning as Non-Convex Optimization

By Andrew Guillory, Erick J. Chastain, J. Bilmes. 2009

20 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sanjay Krishnan
2. M. Franklin
3. Jiannan Wang
4. Eugene Wu
5. Ken Goldberg
6. Ihab F. Ilyas
7. Eric Learned-Miller
8. Haw-Shiuan Chang

Citations in the corpus

• Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples (2017, background): “Notice that if we directly apply bias at the beginning without running burn-in epochs, incorrect examples might be emphasized, which is also known as the local minimum problem in active learning [14].”
• ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “Active Learning (AL) [18].”
• ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “Active Learning largely studies the problem of label acquisition [31], and recently the links between Active Learning and Stochastic optimization have been studied [17].”