10 citations · 53 across the 28 of their papers we have counts for
7 papers · 1 filter
Reliable Learning of Halfspaces under Gaussian Marginals
Ilias Diakonikolas, Lisheng Ren, Nikos Zarifis
We study the problem of PAC learning halfspaces in the reliable agnostic model of Kalai et al. (2012). The reliable PAC model captures learning scenarios where one type of error is…
Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
Puqian Wang, Nikos Zarifis, Ilias Diakonikolas +1
A single-index model (SIM) is a function of the form , where is a known link function and …
Efficient Testable Learning of General Halfspaces with Adversarial Label Noise
Ilias Diakonikolas, Daniel M. Kane, Sihan Liu +1
We study the task of testable learning of general -- not necessarily homogeneous -- halfspaces with adversarial label noise with respect to the Gaussian distribution. In the testab…
Online Learning of Halfspaces with Massart Noise
Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos +1
We study the task of online learning in the presence of Massart noise. Instead of assuming that the online adversary chooses an arbitrary sequence of labels, we assume that the con…
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs
Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2
We study the efficient learnability of low-degree polynomial threshold functions (PTFs) in the presence of a constant fraction of adversarial corruptions. Our main algorithmic resu…
Statistical Query Lower Bounds for Learning Truncated Gaussians
Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas +1
We study the problem of estimating the mean of an identity covariance Gaussian in the truncated setting, in the regime when the truncation set comes from a low-complexity family $\…
Robustly Learning Single-Index Models via Alignment Sharpness
Nikos Zarifis, Puqian Wang, Ilias Diakonikolas +1
We study the problem of learning Single-Index Models under the loss in the agnostic model. We give an efficient learning algorithm, achieving a constant factor approximatio…