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Dan Garber

28 papers · 2011–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (14) 🐝 Cross-Pollinator (11) 🐺 Lone Wolf (6) πŸ”¬ Deep Specialist (15) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (11) πŸ’Ž Century Club (28) πŸ—ƒοΈ Keyword Collector (98)

Conferences

NIPS (9) COLT (6) ICML (6) AISTATS (5) ALT (1) JMLR (1)

Top co-authors

Ben Kretzu (5) Elad Hazan (4) Weiran Wang (3) Nathan Srebro (3) Jialei Wang (3) Atara Kaplan (2) Sham (1) Chi Jin (1) Ohad Shamir (1) Gal Korcia (1)

Keywords

convex optimization (9) frank-wolfe algorithm (5) online learning (5) stochastic optimization (4) projection-free optimization (4) conditional gradient (4) matrix computation (3) regret bound (3) linear convergence (3) principal component analysis (3) online convex optimization (3) eigenvector computation (3) approximation algorithm (2) streaming algorithm (2) frank-wolfe method (2) singular value decomposition (2) canonical correlation analysis (2) low-rank matrix (2) regret minimization (2) matrix recovery (2)

Papers

Blackwell’s Approachability with Approximation Algorithms COLT 2025
Projection-Free Online Convex Optimization with Time-Varying Constraints ICML 2024
Projection-free Online Exp-concave Optimization COLT 2023
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle AISTATS 2023
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity NIPS 2022
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees COLT 2022
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator NIPS 2022
Frank-Wolfe with a Nearest Extreme Point Oracle COLT 2021
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems NIPS 2021
Revisiting Projection-free Online Learning: the Strongly Convex Case AISTATS 2021
Online Convex Optimization in the Random Order Model ICML 2020
Improved Regret Bounds for Projection-free Bandit Convex Optimization AISTATS 2020
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems COLT 2020
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity NIPS 2020
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems AISTATS 2019
Stochastic Canonical Correlation Analysis JMLR 2019
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA COLT 2019
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity AISTATS 2019
Efficient coordinate-wise leading eigenvector computation ALT 2018
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis ICML 2017
Efficient Online Linear Optimization with Approximation Algorithms NIPS 2017
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis NIPS 2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning ICML 2016
Faster Projection-free Convex Optimization over the Spectrahedron NIPS 2016
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes NIPS 2016
Online Learning of Eigenvectors ICML 2015
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets ICML 2015
Approximating Semidefinite Programs in Sublinear Time NIPS 2011