Does an Efficient Calibrated Forecasting Strategy Exist?
Abstract
We recall two previously-proposed notions of asymptotic calibration for a forecaster making a sequence of probability predictions. We note that the existence of efficient algorithms for calibrated forecasting holds only in the case of binary outcomes. We pose the question: do there exist such efficient algorithms for the general (non-binary) case?
Cite
Text
Abernethy and Mannor. "Does an Efficient Calibrated Forecasting Strategy Exist?." Proceedings of the 24th Annual Conference on Learning Theory, 2011.Markdown
[Abernethy and Mannor. "Does an Efficient Calibrated Forecasting Strategy Exist?." Proceedings of the 24th Annual Conference on Learning Theory, 2011.](https://mlanthology.org/colt/2011/abernethy2011colt-efficient/)BibTeX
@inproceedings{abernethy2011colt-efficient,
title = {{Does an Efficient Calibrated Forecasting Strategy Exist?}},
author = {Abernethy, Jacob and Mannor, Shie},
booktitle = {Proceedings of the 24th Annual Conference on Learning Theory},
year = {2011},
pages = {809-812},
volume = {19},
url = {https://mlanthology.org/colt/2011/abernethy2011colt-efficient/}
}