Dan Garber | DeepAI

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∙ 08/03/2023

Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity

We consider convex relaxations for recovering low-rank tensors based on ...

0 Dan Garber, et al. ∙

∙ 02/09/2023

Projection-free Online Exp-concave Optimization

We consider the setting of online convex optimization (OCO) with exp-con...

0 Dan Garber, et al. ∙

∙ 10/25/2022

Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle

This paper considers a convex composite optimization problem with affine...

0 Dan Garber, et al. ∙

∙ 06/23/2022

Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems

Low-rank and nonsmooth matrix optimization problems capture many fundame...

0 Dan Garber, et al. ∙

∙ 06/19/2022

Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator

Tyler's M-estimator is a well known procedure for robust and heavy-taile...

0 Lior Danon, et al. ∙

∙ 02/09/2022

New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees

We present new efficient projection-free algorithms for online convex op...

0 Dan Garber, et al. ∙

∙ 02/08/2022

Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems

Low-rank and nonsmooth matrix optimization problems capture many fundame...

0 Dan Garber, et al. ∙

∙ 02/08/2022

Efficient Algorithms for High-Dimensional Convex Subspace Optimization via Strict Complementarity

We consider optimization problems in which the goal is find a k-dimensio...

0 Dan Garber, et al. ∙

∙ 02/03/2021

Frank-Wolfe with a Nearest Extreme Point Oracle

We consider variants of the classical Frank-Wolfe algorithm for constrai...

0 Dan Garber, et al. ∙

∙ 12/18/2020

On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization

Convex optimization over the spectrahedron, i.e., the set of all real n×...

0 Dan Garber, et al. ∙

∙ 10/15/2020

Revisiting Projection-free Online Learning: the Strongly Convex Case

Projection-free optimization algorithms, which are mostly based on the c...

0 Dan Garber, et al. ∙

∙ 05/31/2020

Revisiting Frank-Wolfe for Polytopes: Strict Complementary and Sparsity

In recent years it was proved that simple modifications of the classical...

0 Dan Garber, et al. ∙

∙ 01/31/2020

On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems

We revisit the use of Stochastic Gradient Descent (SGD) for solving conv...

0 Dan Garber, et al. ∙

∙ 12/03/2019

Linear Convergence of Frank-Wolfe for Rank-One Matrix Recovery Without Strong Convexity

We consider convex optimization problems which are widely used as convex...

0 Dan Garber, et al. ∙

∙ 10/08/2019

Improved Regret Bounds for Projection-free Bandit Convex Optimization

We revisit the challenge of designing online algorithms for the bandit c...

0 Dan Garber, et al. ∙

∙ 02/05/2019

On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems

Smooth convex minimization over the unit trace-norm ball is an important...

0 Dan Garber, et al. ∙

∙ 09/27/2018

On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA

Non-convex optimization with global convergence guarantees is gaining si...

0 Dan Garber, et al. ∙

∙ 09/27/2018

Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems

Composite convex optimization problems which include both a nonsmooth te...

0 Dan Garber, et al. ∙

∙ 02/20/2018

Learning of Optimal Forecast Aggregation in Partial Evidence Environments

We consider the forecast aggregation problem in repeated settings, where...

0 Yakov Babichenko, et al. ∙

∙ 02/15/2018

Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems

Motivated by matrix recovery problems such as Robust Principal Component...

0 Dan Garber, et al. ∙

∙ 02/13/2018

Fast Rates for Online Gradient Descent Without Strong Convexity via Hoffman's Bound

Hoffman's classical result gives a bound on the distance of a point from...

0 Dan Garber, et al. ∙

∙ 02/25/2017

Efficient coordinate-wise leading eigenvector computation

We develop and analyze efficient "coordinate-wise" methods for finding t...

0 Jialei Wang, et al. ∙

∙ 02/21/2017

Stochastic Canonical Correlation Analysis

We tightly analyze the sample complexity of CCA, provide a learning algo...

0 Chao Gao, et al. ∙

∙ 01/20/2013

A Linearly Convergent Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization

Linear optimization is many times algorithmically simpler than non-linea...

0 Dan Garber, et al. ∙

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