ICML 2022spotlight14 citations
Achieving Minimax Rates in Pool-Based Batch Active Learning
Claudio Gentile, Zhilei Wang, Tong Zhang
Abstract
We consider a batch active learning scenario where the learner adaptively issues batches of points to a labeling oracle. Sampling labels in batches is highly desirable in practice due to the smaller number of interactive rounds with the labeling oracle (often human beings). However, batch active learning typically pays the price of a reduced adaptivity, leading to suboptimal results. In this paper we propose a solution which requires a careful trade off between the informativeness of the queried points and their diversity. We theoretically investigate batch active learning in the practically relevant scenario where the unlabeled pool of data is available beforehand (
BibTeX
@InProceedings{pmlr-v162-gentile22a,
title = {Achieving Minimax Rates in Pool-Based Batch Active Learning},
author = {Gentile, Claudio and Wang, Zhilei and Zhang, Tong},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {7339--7367},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/gentile22a/gentile22a.pdf},
url = {https://proceedings.mlr.press/v162/gentile22a.html},
abstract = {We consider a batch active learning scenario where the learner adaptively issues batches of points to a labeling oracle. Sampling labels in batches is highly desirable in practice due to the smaller number of interactive rounds with the labeling oracle (often human beings). However, batch active learning typically pays the price of a reduced adaptivity, leading to suboptimal results. In this paper we propose a solution which requires a careful trade off between the informativeness of the queried points and their diversity. We theoretically investigate batch active learning in the practically relevant scenario where the unlabeled pool of data is available beforehand (