Dan Garber

671 total citations
22 papers, 142 citations indexed

About

Dan Garber is a scholar working on Artificial Intelligence, Computational Mechanics and Management Science and Operations Research. According to data from OpenAlex, Dan Garber has authored 22 papers receiving a total of 142 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 15 papers in Computational Mechanics and 9 papers in Management Science and Operations Research. Recurrent topics in Dan Garber's work include Sparse and Compressive Sensing Techniques (15 papers), Stochastic Gradient Optimization Techniques (10 papers) and Advanced Bandit Algorithms Research (9 papers). Dan Garber is often cited by papers focused on Sparse and Compressive Sensing Techniques (15 papers), Stochastic Gradient Optimization Techniques (10 papers) and Advanced Bandit Algorithms Research (9 papers). Dan Garber collaborates with scholars based in Israel, United States and United Kingdom. Dan Garber's co-authors include Elad Hazan, Edo Liberty, Christos Boutsidis, Zohar Karnin, Tengyu Ma, Nathan Srebro, Ofer Meshi, Weiran Wang, Yakov Babichenko and Ohad Shamir and has published in prestigious journals such as Mathematical Programming, SIAM Journal on Optimization and Mathematics of Operations Research.

In The Last Decade

Dan Garber

22 papers receiving 134 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dan Garber Israel	7	86	75	37	32	28	22	142
Brian Bullins United States	7	98 1.1×	74 1.0×	37 1.0×	11 0.3×	30 1.1×	15	156
Duy Nhat Phan France	8	46 0.5×	71 0.9×	29 0.8×	17 0.5×	14 0.5×	17	122
Chris Junchi Li United States	6	85 1.0×	57 0.8×	11 0.3×	16 0.5×	11 0.4×	14	119
Farbod Roosta-Khorasani United States	3	69 0.8×	48 0.6×	22 0.6×	7 0.2×	15 0.5×	6	113
Yoel Drori Israel	5	77 0.9×	127 1.7×	93 2.5×	11 0.3×	68 2.4×	7	197
Quoc Tran-Dinh United States	10	100 1.2×	129 1.7×	115 3.1×	9 0.3×	56 2.0×	23	231
Pavel Dvurechensky Russia	9	113 1.3×	127 1.7×	65 1.8×	13 0.4×	55 2.0×	38	192
Oliver Hinder United States	4	64 0.7×	67 0.9×	43 1.2×	5 0.2×	37 1.3×	9	132
Kaiyi Ji United States	8	87 1.0×	35 0.5×	5 0.1×	24 0.8×	9 0.3×	21	152
Adi Akavia Israel	9	131 1.5×	52 0.7×	11 0.3×	2 0.1×	56 2.0×	20	200

Countries citing papers authored by Dan Garber

Since Specialization

Citations

This map shows the geographic impact of Dan Garber's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dan Garber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Garber more than expected).

Fields of papers citing papers by Dan Garber

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dan Garber. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dan Garber. The network helps show where Dan Garber may publish in the future.

Co-authorship network of co-authors of Dan Garber

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Garber. A scholar is included among the top collaborators of Dan Garber based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dan Garber. Dan Garber is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Garber, Dan, et al.. (2022). On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization. Mathematics of Operations Research. 1 indexed citations

Garber, Dan, et al.. (2021). Revisiting Projection-free Online Learning: the Strongly Convex Case. International Conference on Artificial Intelligence and Statistics. 3592–3600. 2 indexed citations

Garber, Dan. (2021). On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems. SIAM Journal on Optimization. 31(1). 727–753. 4 indexed citations

Garber, Dan. (2020). Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. Neural Information Processing Systems. 33. 18883–18893. 4 indexed citations

Garber, Dan, et al.. (2020). Improved Regret Bounds for Projection-free Bandit Convex Optimization. arXiv (Cornell University). 2196–2206. 1 indexed citations

Gao, Chao, Dan Garber, Nathan Srebro, Jialei Wang, & Weiran Wang. (2019). Stochastic Canonical Correlation Analysis. arXiv (Cornell University). 20(167). 1–46. 3 indexed citations

Garber, Dan, et al.. (2019). Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. 286–294. 1 indexed citations

Garber, Dan, et al.. (2019). Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems. Mathematical Programming. 186(1-2). 185–208. 1 indexed citations

Garber, Dan, Ohad Shamir, & Nathan Srebro. (2017). Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. International Conference on Machine Learning. 1203–1212. 4 indexed citations

10.

Garber, Dan & Ofer Meshi. (2016). Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. arXiv (Cornell University). 29. 1001–1009. 4 indexed citations

11.

Garber, Dan. (2016). Faster Projection-free Convex Optimization over the Spectrahedron. Neural Information Processing Systems. 29. 874–882. 1 indexed citations

12.

Garber, Dan & Elad Hazan. (2016). A Linearly Convergent Variant of the Conditional Gradient Algorithm under Strong Convexity, with Applications to Online and Stochastic Optimization. SIAM Journal on Optimization. 26(3). 1493–1528. 25 indexed citations

13.

Garber, Dan, Elad Hazan, Chi Jin, et al.. (2016). Faster eigenvector computation via shift-and-invert preconditioning. 2626–2634. 2 indexed citations

14.

Garber, Dan, Elad Hazan, & Tengyu Ma. (2015). Online Learning of Eigenvectors. International Conference on Machine Learning. 560–568. 9 indexed citations

15.

Garber, Dan & Elad Hazan. (2015). Sublinear time algorithms for approximate semidefinite programming. Mathematical Programming. 158(1-2). 329–361. 6 indexed citations

16.

Boutsidis, Christos, Dan Garber, Zohar Karnin, & Edo Liberty. (2014). Online Principal Components Analysis. 887–901. 18 indexed citations

17.

Garber, Dan & Elad Hazan. (2014). Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets. arXiv (Cornell University). 541–549. 31 indexed citations

18.

Garber, Dan & Elad Hazan. (2013). A Polynomial Time Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization. arXiv (Cornell University). 3 indexed citations

19.

Garber, Dan & Elad Hazan. (2013). Playing Non-linear Games with Linear Oracles. 65. 420–428. 7 indexed citations

20.

Garber, Dan & Elad Hazan. (2011). Approximating Semidefinite Programs in Sublinear Time. Neural Information Processing Systems. 24. 1080–1088. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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