NeurIPS 2016poster59 citations
Adaptive Concentration Inequalities for Sequential Decision Problems
Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
Abstract
A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees. We introduce Hoeffding-like concentration inequalities that hold for a random, adaptively chosen number of samples. Our inequalities are tight under natural assumptions and can greatly simplify the analysis of common sequential decision problems. In particular, we apply them to sequential hypothesis testing, best arm identification, and sorting. The resulting algorithms rival or exceed the state of the art both theoretically and empirically.
BibTeX
@inproceedings{NIPS2016_2e65f2f2,
author = {Zhao, Shengjia and Zhou, Enze and Sabharwal, Ashish and Ermon, Stefano},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Adaptive Concentration Inequalities for Sequential Decision Problems},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/2e65f2f2fdaf6c699b223c61b1b5ab89-Paper.pdf},
volume = {29},
year = {2016}
}