2022-06-28-alaa22a.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
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models	Proceedings of the 39th International Conference on Machine Learning	Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the image synthesis setup, exhibit a limited capacity for diagnosing the different modes of failure of generative models across broader application domains. In this paper, we introduce a 3-dimensional evaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies statistical divergence measures with precision-recall analysis, enabling sample- and distribution-level diagnoses of model fidelity and diversity. We introduce generalization as an additional, independent dimension (to the fidelity-diversity trade-off) that quantifies the extent to which a model copies training data{—}a crucial performance indicator when modeling sensitive data with requirements on privacy. The three metric components correspond to (interpretable) probabilistic quantities, and are estimated via sample-level binary classification. The sample-level nature of our metric inspires a novel use case which we call model auditing, wherein we judge the quality of individual samples generated by a (black-box) model, discarding low-quality samples and hence improving the overall model performance in a post-hoc manner.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	alaa22a	0	How Faithful is your Synthetic Data? {S}ample-level Metrics for Evaluating and Auditing Generative Models	290	306	290-306	290	false	Alaa, Ahmed and Van Breugel, Boris and Saveliev, Evgeny S. and van der Schaar, Mihaela	given family Ahmed Alaa given family Boris Van Breugel given family Evgeny S. Saveliev given prefix family Mihaela van der Schaar	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/alaa22a/alaa22a.pdf