7 citations · 7 across the 15 of their papers we have counts for
15 papers
Efficient Optimization on the Euclidean Sphere: Riemannian Gradient Alignment as a Unifying Principle
Puqian Wang, Nikos Zarifis, Jelena Diakonikolas
Euclidean sphere-constrained optimization problems represent perhaps the most basic class of nonconvex-constrained problems for which there exist efficient optimization algorithms…
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…
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…
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas +1
We study the problem of learning a single neuron with respect to the -loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goa…
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 …
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Shuyao Li, Yu Cheng, Ilias Diakonikolas +3
Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learnin…
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…
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2
We study the problem of learning general (i.e., not necessarily homogeneous) halfspaces with Random Classification Noise under the Gaussian distribution. We establish nearly-matchi…
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise
Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2
We study the problem of PAC learning -margin halfspaces with Random Classification Noise. We establish an information-computation tradeoff suggesting an inherent gap between the…
Robustly Learning a Single Neuron via Sharpness
Puqian Wang, Nikos Zarifis, Ilias Diakonikolas +1
We study the problem of learning a single neuron with respect to the -loss in the presence of adversarial label noise. We give an efficient algorithm that, for a broad famil…
Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization
Jelena Diakonikolas, Puqian Wang
Making the gradients small is a fundamental optimization problem that has eluded unifying and simple convergence arguments in first-order optimization, so far primarily reserved fo…