Brian Bullins

640 total citations
15 papers, 156 citations indexed

About

Brian Bullins is a scholar working on Artificial Intelligence, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Brian Bullins has authored 15 papers receiving a total of 156 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 9 papers in Computational Mechanics and 6 papers in Computational Theory and Mathematics. Recurrent topics in Brian Bullins's work include Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (9 papers) and Machine Learning and Algorithms (5 papers). Brian Bullins is often cited by papers focused on Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (9 papers) and Machine Learning and Algorithms (5 papers). Brian Bullins collaborates with scholars based in United States, Israel and Switzerland. Brian Bullins's co-authors include Elad Hazan, Naman Agarwal, Tengyu Ma, Zeyuan Allen-Zhu, Coralia Cartis, Blake Woodworth, Ohad Shamir, Kumar Kshitij Patel, H. Brendan McMahan and Nati Srebro and has published in prestigious journals such as SIAM Journal on Optimization, Linear Algebra and its Applications and Oxford University Research Archive (ORA) (University of Oxford).

In The Last Decade

Brian Bullins

14 papers receiving 146 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Brian Bullins United States 7 98 74 37 30 21 15 156
Dan Garber Israel 7 86 0.9× 75 1.0× 37 1.0× 28 0.9× 19 0.9× 22 142
Mert Gürbüzbalaban United States 7 58 0.6× 54 0.7× 60 1.6× 48 1.6× 19 0.9× 31 174
Pavel Dvurechensky Russia 9 113 1.2× 127 1.7× 65 1.8× 55 1.8× 22 1.0× 38 192
Anna Ma United States 6 144 1.5× 171 2.3× 46 1.2× 90 3.0× 14 0.7× 24 241
Farbod Roosta-Khorasani United States 3 69 0.7× 48 0.6× 22 0.6× 15 0.5× 14 0.7× 6 113
Chris Junchi Li United States 6 85 0.9× 57 0.8× 11 0.3× 11 0.4× 12 0.6× 14 119
Quoc Tran-Dinh United States 10 100 1.0× 129 1.7× 115 3.1× 56 1.9× 20 1.0× 23 231
Yoel Drori Israel 5 77 0.8× 127 1.7× 93 2.5× 68 2.3× 15 0.7× 7 197
Petar Maymounkov United States 4 90 0.9× 58 0.8× 10 0.3× 53 1.8× 102 4.9× 7 217
Stefan Schäffler Germany 7 61 0.6× 12 0.2× 34 0.9× 80 2.7× 6 0.3× 20 175

Countries citing papers authored by Brian Bullins

Since Specialization
Citations

This map shows the geographic impact of Brian Bullins'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 Brian Bullins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Bullins more than expected).

Fields of papers citing papers by Brian Bullins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Bullins. 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 Brian Bullins. The network helps show where Brian Bullins may publish in the future.

Co-authorship network of co-authors of Brian Bullins

This figure shows the co-authorship network connecting the top 25 collaborators of Brian Bullins. A scholar is included among the top collaborators of Brian Bullins 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 Brian Bullins. Brian Bullins is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Woodworth, Blake, Brian Bullins, Ohad Shamir, & Nathan Srebro. (2022). The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 5359–5363. 5 indexed citations
2.
Bullins, Brian, et al.. (2022). Higher-Order Methods for Convex-Concave Min-Max Optimization and Monotone Variational Inequalities. SIAM Journal on Optimization. 32(3). 2208–2229. 10 indexed citations
3.
Bullins, Brian, et al.. (2021). Almost-Linear-Time Weighted 𝓁 p -Norm Solvers in Slightly Dense Graphs via Sparsification.. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 198. 15. 2 indexed citations
4.
Bullins, Brian. (2020). Highly smooth minimization of non-smooth problems. Conference on Learning Theory. 988–1030. 1 indexed citations
5.
Bullins, Brian, et al.. (2020). Adaptive regularization with cubics on manifolds. Oxford University Research Archive (ORA) (University of Oxford). 19 indexed citations
6.
Woodworth, Blake, Kumar Kshitij Patel, Sebastian U. Stich, et al.. (2020). Is Local SGD Better than Minibatch SGD. 1. 10334–10343. 23 indexed citations
7.
Agarwal, Naman, Brian Bullins, Elad Hazan, Sham M. Kakade, & Karan Singh. (2019). Online Control with Adversarial Disturbances. arXiv (Cornell University). 111–119. 9 indexed citations
8.
Agarwal, Naman, Nicolas Boumal, Brian Bullins, & Coralia Cartis. (2018). Adaptive regularization with cubics on manifolds with a first-order analysis. arXiv (Cornell University). 2 indexed citations
9.
Agarwal, Naman, Brian Bullins, Xinyi Chen, et al.. (2018). The Case for Full-Matrix Adaptive Regularization. arXiv (Cornell University).
10.
Agarwal, Naman, Brian Bullins, Xinyi Chen, et al.. (2018). Efficient Full-Matrix Adaptive Regularization. arXiv (Cornell University). 102–110. 2 indexed citations
11.
Agarwal, Naman, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan, & Tengyu Ma. (2017). Finding approximate local minima faster than gradient descent. 1195–1199. 53 indexed citations
12.
Bullins, Brian, Elad Hazan, & Tomer Koren. (2016). The Limits of Learning with Missing Data. Neural Information Processing Systems. 29. 3495–3503. 5 indexed citations
13.
Agarwal, Naman, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, & Tengyu Ma. (2016). Finding Approximate Local Minima for Nonconvex Optimization in Linear Time.. arXiv (Cornell University). 12 indexed citations
14.
Agarwal, Naman, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan, & Tengyu Ma. (2016). Finding Local Minima for Nonconvex Optimization in Linear Time. arXiv (Cornell University). 3 indexed citations
15.
Bolla, Marianna, et al.. (2014). Spectral properties of modularity matrices. Linear Algebra and its Applications. 473. 359–376. 10 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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