7 citations · 7 across the 15 of their papers we have counts for
4 papers · 1 filter
Linear Regression under Missing or Corrupted Coordinates
Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2
We study multivariate linear regression under Gaussian covariates in two settings, where data may be erased or corrupted by an adversary under a coordinate-wise budget. In the inco…
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…
Distributionally Robust Optimization with Adversarial Data Contamination
Shuyao Li, Ilias Diakonikolas, Jelena Diakonikolas
Distributionally Robust Optimization (DRO) provides a framework for decision-making under distributional uncertainty, yet its effectiveness can be compromised by outliers in the tr…
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…