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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 ...
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02/09/2023
Projection-free Online Exp-concave Optimization
We consider the setting of online convex optimization (OCO) with exp-con...
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10/25/2022
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle
This paper considers a convex composite optimization problem with affine...
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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...
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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...
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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...
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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...
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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...
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02/03/2021
Frank-Wolfe with a Nearest Extreme Point Oracle
We consider variants of the classical Frank-Wolfe algorithm for constrai...
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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×...
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10/15/2020
Revisiting Projection-free Online Learning: the Strongly Convex Case
Projection-free optimization algorithms, which are mostly based on the c...
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05/31/2020
Revisiting Frank-Wolfe for Polytopes: Strict Complementary and Sparsity
In recent years it was proved that simple modifications of the classical...
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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...
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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...
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10/08/2019
Improved Regret Bounds for Projection-free Bandit Convex Optimization
We revisit the challenge of designing online algorithms for the bandit c...
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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...
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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...
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09/27/2018
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
Composite convex optimization problems which include both a nonsmooth te...
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02/20/2018
Learning of Optimal Forecast Aggregation in Partial Evidence Environments
We consider the forecast aggregation problem in repeated settings, where...
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02/15/2018
Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems
Motivated by matrix recovery problems such as Robust Principal Component...
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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...
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02/25/2017
Efficient coordinate-wise leading eigenvector computation
We develop and analyze efficient "coordinate-wise" methods for finding t...
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02/21/2017
Stochastic Canonical Correlation Analysis
We tightly analyze the sample complexity of CCA, provide a learning algo...
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01/20/2013