2023-07-02-chung23a.md

abstract	openreview	title	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation. In this context, we introduce the notion of parity calibration, which captures the goal of calibrated forecasting for the increase-decrease (or “parity") event in a timeseries. Parity probabilities can be extracted from a forecasted distribution for the output, but we show that such a strategy leads to theoretical unpredictability and poor practical performance. We then observe that although the original task was regression, parity calibration can be expressed as binary calibration. Drawing on this connection, we use an online binary calibration method to achieve parity calibration. We demonstrate the effectiveness of our approach on real-world case studies in epidemiology, weather forecasting, and model-based control in nuclear fusion.	n10ub5vtVA	Parity calibration	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chung23a	0	Parity calibration	413	423	413-423	413	false	Chung, Youngseog and Rumack, Aaron and Gupta, Chirag	given family Youngseog Chung given family Aaron Rumack given family Chirag Gupta	2023-07-02		Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence	216	inproceedings	date-parts 2023 7 2	https://proceedings.mlr.press/v216/chung23a/chung23a.pdf	label link Supplementary PDF https://proceedings.mlr.press/v216/chung23a/chung23a-supp.pdf