From the 1 of 8 linked papers with an AI index.
8 papers
Linear Regression under Missing or Corrupted Coordinates
Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2
The paper analyzes multivariate linear regression with Gaussian features when an adversary can delete or corrupt a fraction of entries per coordinate, providing tight error bounds…
Efficiently Learning Drifting Halfspaces with Massart Noise
Mingchen Ma, Guyang Cao, Jelena Diakonikolas +1
We study the problem of learning a drifting concept in the presence of Massart noise. In this framework, an online learner has access to a history of independent samples whose labe…
Robust Learning of a Group DRO Neuron
Guyang Cao, Shuyao Li, Sushrut Karmalkar +1
We study the problem of learning a single neuron under standard squared loss in the presence of arbitrary label noise and group-level distributional shifts, for a broad family of 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 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…