22 citations · 29 across the 3 of their papers we have counts for
10 papers · 1 filter
Revisiting Projection-free Online Learning: the Strongly Convex Case
Dan Garber, Ben Kretzu
Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe method, have gained significant interest in the machine learning community in recent ye…
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems
Dan Garber
We revisit the use of Stochastic Gradient Descent (SGD) for solving convex optimization problems that serve as highly popular convex relaxations for many important low-rank matrix…
Improved Regret Bounds for Projection-free Bandit Convex Optimization
Dan Garber, Ben Kretzu
We revisit the challenge of designing online algorithms for the bandit convex optimization problem (BCO) which are also scalable to high dimensional problems. Hence, we consider al…
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.…