7 citations · 7 across the 10 of their papers we have counts for
10 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…
ARO: A New Lens On Matrix Optimization For Large Models
Wenbo Gong, Javier Zazo, Qijun Luo +3
Matrix-based optimizers have attracted growing interest for improving LLM training efficiency, with significant progress centered on orthogonalization/whitening based methods. Whil…
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…
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 …
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…