2021-03-18-anagnostides21a.md

title	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
Robust Learning under Strong Noise via SQs	This work provides several new insights on the robustness of Kearns’ statistical query framework against challenging label-noise models. First, we build on a recent result by \cite{DBLP:journals/corr/abs-2006-04787} that showed noise tolerance of distribution-independently evolvable concept classes under Massart noise. Specifically, we extend their characterization to more general noise models, including the Tsybakov model which considerably generalizes the Massart condition by allowing the flipping probability to be arbitrarily close to $\frac{1}{2}$ for a subset of the domain. As a corollary, we employ an evolutionary algorithm by \cite{DBLP:conf/colt/KanadeVV10} to obtain the first polynomial time algorithm with arbitrarily small excess error for learning linear threshold functions over any spherically symmetric distribution in the presence of spherically symmetric Tsybakov noise. Moreover, we posit access to a stronger oracle, in which for every labeled example we additionally obtain its flipping probability. In this model, we show that every SQ learnable class admits an efficient learning algorithm with $\opt + \epsilon$ misclassification error for a broad class of noise models. This setting substantially generalizes the widely-studied problem of classification under RCN with known noise rate, and corresponds to a non-convex optimization problem even when the noise function – i.e. the flipping probabilities of all points – is known in advance.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	anagnostides21a	0	Robust Learning under Strong Noise via SQs	3808	3816	3808-3816	3808	false	Anagnostides, Ioannis and Gouleakis, Themis and Marashian, Ali	given family Ioannis Anagnostides given family Themis Gouleakis given family Ali Marashian	2021-03-18		Proceedings of The 24th International Conference on Artificial Intelligence and Statistics	130	inproceedings	date-parts 2021 3 18	http://proceedings.mlr.press/v130/anagnostides21a/anagnostides21a.pdf	label link Supplementary PDF http://proceedings.mlr.press/v130/anagnostides21a/anagnostides21a-supp.pdf