6 papers
Robustly Learning Monotone Single-Index Models
Puqian Wang, Nikos Zarifis, Ilias Diakonikolas +1
We consider the basic problem of learning Single-Index Models with respect to the square loss under the Gaussian distribution in the presence of adversarial label noise. Our main c…
Robustly Learning Monotone Generalized Linear Models via Data Augmentation
Nikos Zarifis, Puqian Wang, Ilias Diakonikolas +1
We study the task of learning Generalized Linear models (GLMs) in the agnostic model under the Gaussian distribution. We give the first polynomial-time algorithm that achieves a co…
Robust Learning of Multi-index Models via Iterative Subspace Approximation
Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1
We study the task of learning Multi-Index Models (MIMs) with label noise under the Gaussian distribution. A -MIM is any function that only depends on a -dimensional subsp…
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise
Ilias Diakonikolas, Nikos Zarifis
We study the problem of PAC learning -margin halfspaces in the presence of Massart noise. Without computational considerations, the sample complexity of this learning problem i…
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 $\mathbf{w}^{\ast}…