2024-06-30-hsu24a.md

title	section	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
On the sample complexity of parameter estimation in logistic regression with normal design	Original Papers	The logistic regression model is one of the most popular data generation model in noisy binary classification problems. In this work, we study the sample complexity of estimating the parameters of the logistic regression model up to a given $\ell_2$ error, in terms of the dimension and the inverse temperature, with standard normal covariates. The inverse temperature controls the signal-to-noise ratio of the data generation process. While both generalization bounds and asymptotic performance of the maximum-likelihood estimator for logistic regression are well-studied, the non-asymptotic sample complexity that shows the dependence on error and the inverse temperature for parameter estimation is absent from previous analyses. We show that the sample complexity curve has two change-points in terms of the inverse temperature, clearly separating the low, moderate, and high temperature regimes.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hsu24a	0	On the sample complexity of parameter estimation in logistic regression with normal design	2418	2437	2418-2437	2418	false	Hsu, Daniel and Mazumdar, Arya	given family Daniel Hsu given family Arya Mazumdar	2024-06-30		Proceedings of Thirty Seventh Conference on Learning Theory	247	inproceedings	date-parts 2024 6 30	https://proceedings.mlr.press/v247/hsu24a/hsu24a.pdf