87 citations · 373 across the 33 of their papers we have counts for
34 papers
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…
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…
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle
Dan Garber, Tsur Livney, Shoham Sabach
This paper considers a convex composite optimization problem with affine constraints, which includes problems that take the form of minimizing a smooth convex objective function ov…
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems
Dan Garber, Atara Kaplan
Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning. While significant progress has been made in recent years in d…
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator
Lior Danon, Dan Garber
Tyler's M-estimator is a well known procedure for robust and heavy-tailed covariance estimation. Tyler himself suggested an iterative fixed-point algorithm for computing his estima…
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees
Dan Garber, Ben Kretzu
We present new efficient \textit{projection-free} algorithms for online convex optimization (OCO), where by projection-free we refer to algorithms that avoid computing orthogonal p…