1 citations · 1 across the 3 of their papers we have counts for
3 papers
math.OC2023
From Oja's Algorithm to the Multiplicative Weights Update Method with Applications
Dan Garber
Oja's algorithm is a well known online algorithm studied mainly in the context of stochastic principal component analysis. We make a simple observation, yet to the best of our know…
math.OC2023
Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity
Dan Garber, Atara Kaplan
We consider convex relaxations for recovering low-rank tensors based on constrained minimization over a ball induced by the tensor nuclear norm, recently introduced in \cite{tensor…
cs.LG2023★ 1 cited
Projection-free Online Exp-concave Optimization
Dan Garber, Ben Kretzu
We consider the setting of online convex optimization (OCO) with \textit{exp-concave} losses. The best regret bound known for this setting is , where is the dimens…