@inproceedings{ash2020deep,
author = {Ash, Jordan T. and Zhang, Chicheng and Krishnamurthy, Akshay and Langford, John and Agarwal, Alekh},
title = {Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds},
booktitle = {Eighth International Conference on Learning Representations (ICLR)},
year = {2020},
month = {April},
abstract = {We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.},
url = {http://approjects.co.za/?big=en-us/research/publication/deep-batch-active-learning-by-diverse-uncertain-gradient-lower-bounds/},
}