Approximating Semidefinite Programs in Sublinear Time. - Dan Garber; Elad Hazan (NIPS, 2011)

Universal MMSE Filtering With Logarithmic Adaptive Regret - Dan Garber; Elad Hazan (CoRR, 2011)

Almost Optimal Sublinear Time Algorithm for Semidefinite Programming - Dan Garber; Elad Hazan (CoRR, 2012)

Playing Non-linear Games with Linear Oracles. - Dan Garber; Elad Hazan (FOCS, 2013)

A Polynomial Time Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization - Dan Garber; Elad Hazan (CoRR, 2013)

Adaptive Universal Linear Filtering. - Dan Garber; Elad Hazan (IEEE Trans. Signal Process., 2013)

Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets. - Dan Garber; Elad Hazan (ICML, 2015)

Online Learning of Eigenvectors. - Dan Garber; Elad Hazan; Tengyu Ma 001 (ICML, 2015)

Online Principal Components Analysis. - Christos Boutsidis; Dan Garber; Zohar Shay Karnin; Edo Liberty (SODA, 2015)

Fast and Simple PCA via Convex Optimization. - Dan Garber; Elad Hazan (CoRR, 2015)

Faster Eigenvector Computation via Shift-and-Invert Preconditioning. - Dan Garber; Elad Hazan; Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Faster Projection-free Convex Optimization over the Spectrahedron. - Dan Garber (NIPS, 2016)

Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. - Dan Garber; Ofer Meshi (NIPS, 2016)

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. - Weiran Wang; Jialei Wang; Dan Garber; Nati Srebro (NIPS, 2016)

Sublinear time algorithms for approximate semidefinite programming. - Dan Garber; Elad Hazan (Math. Program., 2016)

A Linearly Convergent Variant of the Conditional Gradient Algorithm under Strong Convexity, with Applications to Online and Stochastic Optimization. - Dan Garber; Elad Hazan (SIAM J. Optim., 2016)

Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. - Dan Garber; Ohad Shamir; Nathan Srebro (ICML, 2017)

Efficient coordinate-wise leading eigenvector computation. - Jialei Wang; Weiran Wang; Dan Garber; Nathan Srebro (ALT, 2018)

Fast Rates for Online Gradient Descent Without Strong Convexity via Hoffman's Bound. - Dan Garber (CoRR, 2018)

Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems. - Dan Garber; Shoham Sabach; Atara Kaplan (CoRR, 2018)

Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity. - Dan Garber (AISTATS, 2019)

Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. - Dan Garber; Atara Kaplan (AISTATS, 2019)

On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA. - Dan Garber (COLT, 2019)

Stochastic Canonical Correlation Analysis. - Chao Gao; Dan Garber; Nathan Srebro; Jialei Wang; Weiran Wang (J. Mach. Learn. Res., 2019)

Improved Regret Bounds for Projection-free Bandit Convex Optimization. - Dan Garber; Ben Kretzu (AISTATS, 2020)

On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems. - Dan Garber (COLT, 2020)

Online Convex Optimization in the Random Order Model. - Dan Garber; Gal Korcia; Kfir Y. Levy (ICML, 2020)

Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. - Dan Garber (NeurIPS, 2020)

Revisiting Projection-free Online Learning: the Strongly Convex Case. - Ben Kretzu; Dan Garber (AISTATS, 2021)

Frank-Wolfe with a Nearest Extreme Point Oracle. - Dan Garber; Noam Wolf (COLT, 2021)

Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. - Atara Kaplan; Dan Garber (NeurIPS, 2021)

Learning Optimal Forecast Aggregation in Partial Evidence Environments. - Yakov Babichenko; Dan Garber (Math. Oper. Res., 2021)

Efficient Online Linear Optimization with Approximation Algorithms. - Dan Garber (Math. Oper. Res., 2021)

Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems. - Dan Garber; Atara Kaplan; Shoham Sabach (Math. Program., 2021)

On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems. - Dan Garber (SIAM J. Optim., 2021)

New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees. - Dan Garber; Ben Kretzu (COLT, 2022)

Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity. - Ron Fisher 003; Dan Garber (NeurIPS, 2022)

Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. - Lior Danon; Dan Garber (NeurIPS, 2022)

Efficient Algorithms for High-Dimensional Convex Subspace Optimization via Strict Complementarity. - Dan Garber; Ron Fisher 003 (CoRR, 2022)

Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems. - Dan Garber; Atara Kaplan (CoRR, 2022)

Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. - Dan Garber; Tsur Livney; Shoham Sabach (AISTATS, 2023)

Projection-free Online Exp-concave Optimization. - Dan Garber; Ben Kretzu (COLT, 2023)

Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity. - Dan Garber; Atara Kaplan (CoRR, 2023)

From Oja's Algorithm to the Multiplicative Weights Update Method with Applications. - Dan Garber (CoRR, 2023)

On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization. - Dan Garber; Atara Kaplan (Math. Oper. Res., 2023)

Linear convergence of Frank-Wolfe for rank-one matrix recovery without strong convexity. - Dan Garber (Math. Program., 2023)

Projection-Free Online Convex Optimization with Time-Varying Constraints. - Dan Garber; Ben Kretzu (ICML, 2024)

Blackwell's Approachability with Approximation Algorithms. - Dan Garber; Mhna Massalha (COLT, 2025)

Low-rank extragradient methods for scalable semidefinite optimization. - Dan Garber; Atara Kaplan (Oper. Res. Lett., 2025)

A Linearly Convergent Frank-Wolfe-type Method for Smooth Convex Minimization over the Spectrahedron. - Dan Garber (CoRR, 2025)

First-Order Sparse Convex Optimization: Better Rates with Sparse Updates. - Dan Garber (CoRR, 2025)