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Dan Garber
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2020 – today
- 2024
- [c28]Dan Garber, Ben Kretzu:
Projection-Free Online Convex Optimization with Time-Varying Constraints. ICML 2024 - [i36]Dan Garber, Ben Kretzu:
Projection-Free Online Convex Optimization with Time-Varying Constraints. CoRR abs/2402.08799 (2024) - [i35]Dan Garber, Atara Kaplan:
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization. CoRR abs/2402.09081 (2024) - 2023
- [j9]Dan Garber
:
Linear convergence of Frank-Wolfe for rank-one matrix recovery without strong convexity. Math. Program. 199(1): 87-121 (2023) - [c27]Dan Garber, Tsur Livney, Shoham Sabach:
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. AISTATS 2023: 7213-7238 - [c26]Dan Garber, Ben Kretzu:
Projection-free Online Exp-concave Optimization. COLT 2023: 1259-1284 - [i34]Dan Garber, Ben Kretzu:
Projection-free Online Exp-concave Optimization. CoRR abs/2302.04859 (2023) - [i33]Dan Garber, Atara Kaplan:
Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity. CoRR abs/2308.01677 (2023) - [i32]Dan Garber:
From Oja's Algorithm to the Multiplicative Weights Update Method with Applications. CoRR abs/2310.15559 (2023) - 2022
- [c25]Dan Garber, Ben Kretzu:
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees. COLT 2022: 2326-2359 - [c24]Ron Fisher, Dan Garber:
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity. NeurIPS 2022 - [c23]Lior Danon, Dan Garber:
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. NeurIPS 2022 - [i31]Dan Garber, Ron Fisher:
Efficient Algorithms for High-Dimensional Convex Subspace Optimization via Strict Complementarity. CoRR abs/2202.04020 (2022) - [i30]Dan Garber, Atara Kaplan:
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. CoRR abs/2202.04026 (2022) - [i29]Dan Garber, Ben Kretzu:
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees. CoRR abs/2202.04721 (2022) - [i28]Lior Danon, Dan Garber:
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. CoRR abs/2206.09370 (2022) - [i27]Dan Garber, Atara Kaplan:
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems. CoRR abs/2206.11523 (2022) - [i26]Dan Garber, Tsur Livney, Shoham Sabach:
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. CoRR abs/2210.13968 (2022) - 2021
- [j8]Dan Garber
Efficient Online Linear Optimization with Approximation Algorithms. Math. Oper. Res. 46(1): 204-220 (2021) - [j7]Yakov Babichenko
Learning Optimal Forecast Aggregation in Partial Evidence Environments. Math. Oper. Res. 46(2): 628-641 (2021) - [j6]Dan Garber, Atara Kaplan, Shoham Sabach
Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems. Math. Program. 186(1): 185-208 (2021) - [j5]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): 727-753 (2021) - [c22]Ben Kretzu, Dan Garber:
Revisiting Projection-free Online Learning: the Strongly Convex Case. AISTATS 2021: 3592-3600 - [c21]Dan Garber, Noam Wolf:
Frank-Wolfe with a Nearest Extreme Point Oracle. COLT 2021: 2103-2132 - [c20]Atara Kaplan, Dan Garber:
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. NeurIPS 2021: 26332-26344 - [i25]Dan Garber, Noam Wolf:
Frank-Wolfe with a Nearest Extreme Point Oracle. CoRR abs/2102.02029 (2021) - 2020
- [c19]Dan Garber, Ben Kretzu:
Improved Regret Bounds for Projection-free Bandit Convex Optimization. AISTATS 2020: 2196-2206 - [c18]Dan Garber:
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems. COLT 2020: 1666-1681 - [c17]Dan Garber, Gal Korcia, Kfir Y. Levy:
Online Convex Optimization in the Random Order Model. ICML 2020: 3387-3396 - [c16]Dan Garber:
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. NeurIPS 2020 - [i24]Dan Garber:
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems. CoRR abs/2001.11668 (2020) - [i23]Dan Garber:
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. CoRR abs/2006.00558 (2020) - [i22]Dan Garber, Ben Kretzu:
Revisiting Projection-free Online Learning: the Strongly Convex Case. CoRR abs/2010.07572 (2020) - [i21]Dan Garber, Atara Kaplan:
On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization. CoRR abs/2012.10469 (2020)
2010 – 2019
- 2019
- [j4]Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang:
Stochastic Canonical Correlation Analysis. J. Mach. Learn. Res. 20: 167:1-167:46 (2019) - [c15]Dan Garber, Atara Kaplan:
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. AISTATS 2019: 286-294 - [c14]Dan Garber:
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity. AISTATS 2019: 295-303 - [c13]Dan Garber:
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA. COLT 2019: 1349-1373 - [i20]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 abs/1902.01644 (2019) - [i19]Dan Garber, Ben Kretzu:
Improved Regret Bounds for Projection-free Bandit Convex Optimization. CoRR abs/1910.03374 (2019) - [i18]Dan Garber:
Linear Convergence of Frank-Wolfe for Rank-One Matrix Recovery Without Strong Convexity. CoRR abs/1912.01467 (2019) - 2018
- [c12]Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro:
Efficient coordinate-wise leading eigenvector computation. ALT 2018: 806-820 - [i17]Dan Garber:
Fast Rates for Online Gradient Descent Without Strong Convexity via Hoffman's Bound. CoRR abs/1802.04623 (2018) - [i16]Dan Garber, Shoham Sabach, Atara Kaplan:
Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems. CoRR abs/1802.05581 (2018) - [i15]Yakov Babichenko, Dan Garber:
Learning of Optimal Forecast Aggregation in Partial Evidence Environments. CoRR abs/1802.07107 (2018) - [i14]Dan Garber, Atara Kaplan:
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. CoRR abs/1809.10477 (2018) - [i13]Dan Garber:
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA. CoRR abs/1809.10491 (2018) - 2017
- [c11]Dan Garber, Ohad Shamir, Nathan Srebro:
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. ICML 2017: 1203-1212 - [c10]Dan Garber:
Efficient Online Linear Optimization with Approximation Algorithms. NIPS 2017: 627-635 - [i12]Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang:
Stochastic Canonical Correlation Analysis. CoRR abs/1702.06533 (2017) - [i11]Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro:
Efficient coordinate-wise leading eigenvector computation. CoRR abs/1702.07834 (2017) - [i10]Dan Garber, Ohad Shamir, Nathan Srebro:
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. CoRR abs/1702.08169 (2017) - [i9]Dan Garber:
Efficient Online Linear Optimization with Approximation Algorithms. CoRR abs/1709.03093 (2017) - 2016
- [j3]Dan Garber, Elad Hazan
Sublinear time algorithms for approximate semidefinite programming. Math. Program. 158(1-2): 329-361 (2016) - [j2]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): 1493-1528 (2016) - [c9]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: 2626-2634 - [c8]Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro:
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. NIPS 2016: 766-774 - [c7]Dan Garber:
Faster Projection-free Convex Optimization over the Spectrahedron. NIPS 2016: 874-882 - [c6]Dan Garber, Ofer Meshi:
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. NIPS 2016: 1001-1009 - [i8]Dan Garber:
Faster Projection-free Convex Optimization over the Spectrahedron. CoRR abs/1605.06203 (2016) - [i7]Dan Garber, Ofer Meshi:
Linear-memory and Decomposition-invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. CoRR abs/1605.06492 (2016) - [i6]Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR abs/1605.08754 (2016) - 2015
- [c5]Dan Garber, Elad Hazan:
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets. ICML 2015: 541-549 - [c4]Dan Garber, Elad Hazan, Tengyu Ma:
Online Learning of Eigenvectors. ICML 2015: 560-568 - [c3]Christos Boutsidis, Dan Garber, Zohar Shay Karnin, Edo Liberty:
Online Principal Components Analysis. SODA 2015: 887-901 - [i5]Dan Garber, Elad Hazan:
Fast and Simple PCA via Convex Optimization. CoRR abs/1509.05647 (2015) - 2014
- [i4]Dan Garber, Elad Hazan:
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets. CoRR abs/1406.1305 (2014) - 2013
- [j1]Dan Garber, Elad Hazan
Adaptive Universal Linear Filtering. IEEE Trans. Signal Process. 61(7): 1595-1604 (2013) - [c2]Dan Garber, Elad Hazan
Playing Non-linear Games with Linear Oracles. FOCS 2013: 420-428 - [i3]Dan Garber, Elad Hazan:
A Polynomial Time Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization. CoRR abs/1301.4666 (2013) - 2012
- [i2]Dan Garber, Elad Hazan:
Almost Optimal Sublinear Time Algorithm for Semidefinite Programming. CoRR abs/1208.5211 (2012) - 2011
- [c1]Dan Garber, Elad Hazan:
Approximating Semidefinite Programs in Sublinear Time. NIPS 2011: 1080-1088 - [i1]Dan Garber, Elad Hazan:
Universal MMSE Filtering With Logarithmic Adaptive Regret. CoRR abs/1111.1136 (2011)
Coauthor Index
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last updated on 2024-09-13 00:41 CEST by the dblp team
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