2022-06-28-angelopoulos22a.md

title	booktitle	abstract	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
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging	Proceedings of the 39th International Conference on Machine Learning	Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model’s mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems. In particular, we show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value with a user-specified confidence probability. Our methods work in conjunction with any base machine learning model, such as a neural network, and endow it with formal mathematical guarantees{—}regardless of the true unknown data distribution or choice of model. Furthermore, they are simple to implement and computationally inexpensive. We evaluate our procedure on three image-to-image regression tasks: quantitative phase microscopy, accelerated magnetic resonance imaging, and super-resolution transmission electron microscopy of a Drosophila melanogaster brain.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	angelopoulos22a	0	Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging	717	730	717-730	717	false	Angelopoulos, Anastasios N and Kohli, Amit Pal and Bates, Stephen and Jordan, Michael and Malik, Jitendra and Alshaabi, Thayer and Upadhyayula, Srigokul and Romano, Yaniv	given family Anastasios N Angelopoulos given family Amit Pal Kohli given family Stephen Bates given family Michael Jordan given family Jitendra Malik given family Thayer Alshaabi given family Srigokul Upadhyayula given family Yaniv Romano	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/angelopoulos22a/angelopoulos22a.pdf