Dan Garber

718 citations
23 papers · 157 · h-index 7

Impact in

Papers in

Dan Garber

23 papers receiving 148 citations

Peers

Dan Garber
Comparison fields: 5 of 38
  • Numerical Analysis 38
  • Computational Mathematics 4
  • Computational Mechanics 82
  • Artificial Intelligence 95
  • Management Science and Operations Research 33
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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-authors

The 18 scholars most cited alongside Dan Garber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Garber Line = papers co-authored together Dan Garber links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201431
2 201626
3 201418
4
Online principal components analysis
201614
5
Approximating Semidefinite Programs in Sublinear Time
201111
6
Online Learning of Eigenvectors
20159
7 20137
8 20156
9
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis
20174
10
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
20204
11
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
20164
12 20214
13
A Polynomial Time Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization
20133
14
Stochastic Canonical Correlation Analysis
20193
15
Revisiting Projection-free Online Learning: the Strongly Convex Case
20212
16 20212
17
Faster eigenvector computation via shift-and-invert preconditioning
20162
18 20222
19
Improved Regret Bounds for Projection-free Bandit Convex Optimization
20201
20
Faster Projection-free Convex Optimization over the Spectrahedron
20161

About Dan Garber

Dan Garber is a scholar working on Artificial Intelligence, Computational Mechanics, Management Science and Operations Research, Numerical Analysis and Computer Networks and Communications, having authored 23 papers that have together received 157 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (16 papers), Stochastic Gradient Optimization Techniques (11 papers), Advanced Bandit Algorithms Research (9 papers), Advanced Optimization Algorithms Research (7 papers), Machine Learning and Algorithms (6 papers), Blind Source Separation Techniques (3 papers), Medical Image Segmentation Techniques (2 papers) and Optimization and Search Problems (2 papers). The work is most often cited by research in Numerical Analysis (38 citations), Computational Mathematics (4 citations), Computational Mechanics (82 citations), Artificial Intelligence (95 citations) and Management Science and Operations Research (33 citations). Dan Garber has collaborated with scholars based in Israel, United States and Japan. Frequent co-authors include Elad Hazan, Christos Boutsidis, Edo Liberty, Zohar Karnin, Tengyu Ma, Nathan Srebro, Ofer Meshi, Weiran Wang, Chao Gao and Ohad Shamir. Their work appears in journals such as Mathematical Programming, Mathematics of Operations Research, SIAM Journal on Optimization, International Conference on Machine Learning and arXiv (Cornell University).

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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