22 citations · 30 across the 5 of their papers we have counts for
5 papers · 1 filter
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
Dan Garber, Atara Kaplan
Composite convex optimization problems which include both a nonsmooth term and a low-rank promoting term have important applications in machine learning and signal processing, such…
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA
Dan Garber
In this paper we focus on the problem of Online Principal Component Analysis in the regret minimization framework. For this problem, all existing regret minimization algorithms for…
Learning of Optimal Forecast Aggregation in Partial Evidence Environments
Yakov Babichenko, Dan Garber
We consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event.…
Improved Complexities of Conditional Gradient-Type Methods with Applications to Robust Matrix Recovery Problems
Dan Garber, Shoham Sabach, Atara Kaplan
Motivated by robust matrix recovery problems such as Robust Principal Component Analysis, we consider a general optimization problem of minimizing a smooth and strongly convex loss…
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity
Dan Garber
Hoffman's classical result gives a bound on the distance of a point from a convex and compact polytope in terms of the magnitude of violation of the constraints. Recently, several…