Dan Garber

Orcid: 0000-0002-5181-9193

According to our database1, Dan Garber authored at least 48 papers between 2011 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization.
CoRR, 2024

Projection-Free Online Convex Optimization with Time-Varying Constraints.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Linear convergence of Frank-Wolfe for rank-one matrix recovery without strong convexity.
Math. Program., May, 2023

From Oja's Algorithm to the Multiplicative Weights Update Method with Applications.
CoRR, 2023

Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity.
CoRR, 2023

Projection-free Online Exp-concave Optimization.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems.
CoRR, 2022

Efficient Algorithms for High-Dimensional Convex Subspace Optimization via Strict Complementarity.
CoRR, 2022

Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
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., 2021

Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems.
Math. Program., 2021

Efficient Online Linear Optimization with Approximation Algorithms.
Math. Oper. Res., 2021

Learning Optimal Forecast Aggregation in Partial Evidence Environments.
Math. Oper. Res., 2021

Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Frank-Wolfe with a Nearest Extreme Point Oracle.
Proceedings of the Conference on Learning Theory, 2021

Revisiting Projection-free Online Learning: the Strongly Convex Case.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization.
CoRR, 2020

Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Online Convex Optimization in the Random Order Model.
Proceedings of the 37th International Conference on Machine Learning, 2020

On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems.
Proceedings of the Conference on Learning Theory, 2020

Improved Regret Bounds for Projection-free Bandit Convex Optimization.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Stochastic Canonical Correlation Analysis.
J. Mach. Learn. Res., 2019

On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA.
Proceedings of the Conference on Learning Theory, 2019

Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems.
CoRR, 2018

Fast Rates for Online Gradient Descent Without Strong Convexity via Hoffman's Bound.
CoRR, 2018

Efficient coordinate-wise leading eigenvector computation.
Proceedings of the Algorithmic Learning Theory, 2018

2017
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
A Linearly Convergent Variant of the Conditional Gradient Algorithm under Strong Convexity, with Applications to Online and Stochastic Optimization.
SIAM J. Optim., 2016

Sublinear time algorithms for approximate semidefinite programming.
Math. Program., 2016

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Faster Projection-free Convex Optimization over the Spectrahedron.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Faster Eigenvector Computation via Shift-and-Invert Preconditioning.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Fast and Simple PCA via Convex Optimization.
CoRR, 2015

Online Principal Components Analysis.
Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, 2015

Online Learning of Eigenvectors.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2013
Adaptive Universal Linear Filtering.
IEEE Trans. Signal Process., 2013

A Polynomial Time Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization
CoRR, 2013

Playing Non-linear Games with Linear Oracles.
Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science, 2013

2012
Almost Optimal Sublinear Time Algorithm for Semidefinite Programming
CoRR, 2012

2011
Universal MMSE Filtering With Logarithmic Adaptive Regret
CoRR, 2011

Approximating Semidefinite Programs in Sublinear Time.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011


  Loading...