Dan Garber

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 65

Top Topics

Online Learning

Principal Component Analysis

Convex Optimization

Top Venues

Publications

Dan Garber, Atara Kaplan
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization. CoRR (2024)
Dan Garber, Ben Kretzu
Projection-Free Online Convex Optimization with Time-Varying Constraints. CoRR (2024)
Dan Garber, Atara Kaplan
Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity. CoRR (2023)
Dan Garber
Linear convergence of Frank-Wolfe for rank-one matrix recovery without strong convexity. Math. Program. 199 (1) (2023)
Dan Garber, Tsur Livney, Shoham Sabach
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. AISTATS (2023)
Dan Garber, Ben Kretzu
Projection-free Online Exp-concave Optimization. CoRR (2023)
Dan Garber, Ben Kretzu
Projection-free Online Exp-concave Optimization. COLT (2023)
Dan Garber
From Oja's Algorithm to the Multiplicative Weights Update Method with Applications. CoRR (2023)
Dan Garber, Ben Kretzu
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees. COLT (2022)
Lior Danon, Dan Garber
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. CoRR (2022)
Dan Garber, Atara Kaplan
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems. CoRR (2022)
Dan Garber, Ron Fisher
Efficient Algorithms for High-Dimensional Convex Subspace Optimization via Strict Complementarity. CoRR (2022)
Dan Garber, Tsur Livney, Shoham Sabach
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. CoRR (2022)
Lior Danon, Dan Garber
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. NeurIPS (2022)
Ron Fisher, Dan Garber
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity. NeurIPS (2022)
Dan Garber, Atara Kaplan
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. CoRR (2022)
Dan Garber, Ben Kretzu
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees. CoRR (2022)
Yakov Babichenko, Dan Garber
Learning Optimal Forecast Aggregation in Partial Evidence Environments. Math. Oper. Res. 46 (2) (2021)
Dan Garber
On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems. SIAM J. Optim. 31 (1) (2021)
Dan Garber, Noam Wolf
Frank-Wolfe with a Nearest Extreme Point Oracle. COLT (2021)
Dan Garber, Atara Kaplan, Shoham Sabach
Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems. Math. Program. 186 (1) (2021)
Ben Kretzu, Dan Garber
Revisiting Projection-free Online Learning: the Strongly Convex Case. AISTATS (2021)
Dan Garber, Noam Wolf
Frank-Wolfe with a Nearest Extreme Point Oracle. CoRR (2021)
Atara Kaplan, Dan Garber
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. NeurIPS (2021)
Dan Garber
Efficient Online Linear Optimization with Approximation Algorithms. Math. Oper. Res. 46 (1) (2021)
Dan Garber
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems. CoRR (2020)
Dan Garber
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. NeurIPS (2020)
Dan Garber, Atara Kaplan
On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization. CoRR (2020)
Dan Garber, Ben Kretzu
Revisiting Projection-free Online Learning: the Strongly Convex Case. CoRR (2020)
Dan Garber, Gal Korcia, Kfir Y. Levy
Online Convex Optimization in the Random Order Model. ICML (2020)
Dan Garber
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems. COLT (2020)
Dan Garber
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. CoRR (2020)
Dan Garber, Ben Kretzu
Improved Regret Bounds for Projection-free Bandit Convex Optimization. AISTATS (2020)
Dan Garber
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity. AISTATS (2019)
Dan Garber
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA. COLT (2019)
Dan Garber, Atara Kaplan
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. AISTATS (2019)
Dan Garber
Linear Convergence of Frank-Wolfe for Rank-One Matrix Recovery Without Strong Convexity. CoRR (2019)
Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang
Stochastic Canonical Correlation Analysis. J. Mach. Learn. Res. 20 (2019)
Dan Garber, Ben Kretzu
Improved Regret Bounds for Projection-free Bandit Convex Optimization. CoRR (2019)
Dan Garber
On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems. CoRR (2019)
Dan Garber, Atara Kaplan
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. CoRR (2018)
Dan Garber
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA. CoRR (2018)
Dan Garber
Fast Rates for Online Gradient Descent Without Strong Convexity via Hoffman's Bound. CoRR (2018)
Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro
Efficient coordinate-wise leading eigenvector computation. ALT (2018)
Dan Garber, Shoham Sabach, Atara Kaplan
Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems. CoRR (2018)
Yakov Babichenko, Dan Garber
Learning of Optimal Forecast Aggregation in Partial Evidence Environments. CoRR (2018)
Dan Garber, Ohad Shamir, Nathan Srebro
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. CoRR (2017)
Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang
Stochastic Canonical Correlation Analysis. CoRR (2017)
Dan Garber, Ohad Shamir, Nathan Srebro
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. ICML (2017)
Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro
Efficient coordinate-wise leading eigenvector computation. CoRR (2017)
Dan Garber
Efficient Online Linear Optimization with Approximation Algorithms. CoRR (2017)
Dan Garber
Efficient Online Linear Optimization with Approximation Algorithms. NIPS (2017)
Dan Garber, Ofer Meshi
Linear-memory and Decomposition-invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. CoRR (2016)
Dan Garber
Faster Projection-free Convex Optimization over the Spectrahedron. NIPS (2016)
Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR (2016)
Dan Garber, Ofer Meshi
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. NIPS (2016)
Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. ICML (2016)
Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. NIPS (2016)
Dan Garber, Elad Hazan
A Linearly Convergent Variant of the Conditional Gradient Algorithm under Strong Convexity, with Applications to Online and Stochastic Optimization. SIAM J. Optim. 26 (3) (2016)
Dan Garber, Elad Hazan
Sublinear time algorithms for approximate semidefinite programming. Math. Program. 158 (1-2) (2016)
Dan Garber
Faster Projection-free Convex Optimization over the Spectrahedron. CoRR (2016)
Christos Boutsidis, Dan Garber, Zohar Shay Karnin, Edo Liberty
Online Principal Components Analysis. SODA (2015)
Dan Garber, Elad Hazan
Fast and Simple PCA via Convex Optimization. CoRR (2015)
Dan Garber, Elad Hazan
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets. ICML (2015)
Dan Garber, Elad Hazan, Tengyu Ma
Online Learning of Eigenvectors. ICML (2015)
Dan Garber, Elad Hazan
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets. CoRR (2014)
Dan Garber, Elad Hazan
Playing Non-linear Games with Linear Oracles. FOCS (2013)
Dan Garber, Elad Hazan
A Polynomial Time Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization CoRR (2013)
Dan Garber, Elad Hazan
Adaptive Universal Linear Filtering. IEEE Trans. Signal Process. 61 (7) (2013)
Dan Garber, Elad Hazan
Almost Optimal Sublinear Time Algorithm for Semidefinite Programming CoRR (2012)
Dan Garber, Elad Hazan
Universal MMSE Filtering With Logarithmic Adaptive Regret CoRR (2011)
Dan Garber, Elad Hazan
Approximating Semidefinite Programs in Sublinear Time. NIPS (2011)