2024-10-09-hirzel24a.md

title	openreview	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
Training and Cross-Validating Machine Learning Pipelines with Limited Memory	4LkaPSHUQQ	While automated machine learning (AutoML) can save human labor in finding well-performing pipelines, it often suffers from two problems: overfitting and using excessive resources. Unfortunately, the solutions are often at odds: cross-validation helps reduce overfitting at the expense of more resources; conversely, preprocessing on a separate compute cluster and then cross-validating only the final predictor saves resources at the expense of more overfitting. This paper shows how to train and cross-validate entire pipelines on a single moderate machine with limited memory by using monoids, which are associative, thus providing a flexible way for handling large data one batch at a time. To facilitate AutoML, our approach is designed to support the common sklearn APIs used by many AutoML systems for pipelines, training, cross-validation, and several operators. Abstracted behind those APIs, our approach uses task graphs to extend the benefits of monoids from operators to pipelines, and provides a multi-backend implementation. Overall, our approach lets users train and cross-validate pipelines on simple and inexpensive compute infrastructure.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hirzel24a	0	Training and Cross-Validating Machine Learning Pipelines with Limited Memory	13/1	25	13/1-25	13	false	Hirzel, Martin and Kate, Kiran and Mandel, Louis and Shinnar, Avraham	given family Martin Hirzel given family Kiran Kate given family Louis Mandel given family Avraham Shinnar	2024-10-09		Proceedings of the Third International Conference on Automated Machine Learning	256	inproceedings	date-parts 2024 10 9	https://raw.githubusercontent.com/mlresearch/v256/main/assets/hirzel24a/hirzel24a.pdf