Aaron Sidford

1.3k total citations
20 papers, 245 citations indexed

About

Aaron Sidford is a scholar working on Artificial Intelligence, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Aaron Sidford has authored 20 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 8 papers in Computational Mechanics and 8 papers in Computational Theory and Mathematics. Recurrent topics in Aaron Sidford's work include Stochastic Gradient Optimization Techniques (14 papers), Sparse and Compressive Sensing Techniques (7 papers) and Machine Learning and Algorithms (5 papers). Aaron Sidford is often cited by papers focused on Stochastic Gradient Optimization Techniques (14 papers), Sparse and Compressive Sensing Techniques (7 papers) and Machine Learning and Algorithms (5 papers). Aaron Sidford collaborates with scholars based in United States and United Kingdom. Aaron Sidford's co-authors include Yin Tat Lee, Jonathan A. Kelner, Lorenzo Orecchia, Gary L. Miller, Jakub Pachocki, Michael B. Cohen, Sham M. Kakade, Mengdi Wang, Yinyu Ye and Xian Wu and has published in prestigious journals such as Naval Research Logistics (NRL), arXiv (Cornell University) and neural information processing systems.

In The Last Decade

Aaron Sidford

19 papers receiving 229 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aaron Sidford United States 8 146 90 56 52 30 20 245
Ashwin Pananjady United States 8 99 0.7× 47 0.5× 69 1.2× 45 0.9× 58 1.9× 28 256
Andre Wibisono United States 6 210 1.4× 32 0.4× 59 1.1× 84 1.6× 36 1.2× 13 304
Rasmus Kyng United States 8 79 0.5× 122 1.4× 55 1.0× 20 0.4× 39 1.3× 17 217
Aryeh Kontorovich Israel 8 114 0.8× 21 0.2× 58 1.0× 26 0.5× 26 0.9× 34 245
Alexander Nazin Russia 8 78 0.5× 29 0.3× 23 0.4× 37 0.7× 41 1.4× 23 249
Haihao Lu United States 7 85 0.6× 53 0.6× 38 0.7× 118 2.3× 10 0.3× 23 243
Victor Bittorf United States 4 145 1.0× 24 0.3× 58 1.0× 70 1.3× 8 0.3× 5 254
Tomer Koren Israel 9 202 1.4× 34 0.4× 60 1.1× 42 0.8× 13 0.4× 30 309
Morteza Monemizadeh United States 9 161 1.1× 138 1.5× 112 2.0× 46 0.9× 8 0.3× 24 303
Yair Carmon United States 7 130 0.9× 45 0.5× 13 0.2× 114 2.2× 14 0.5× 16 215

Countries citing papers authored by Aaron Sidford

Since Specialization
Citations

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

Fields of papers citing papers by Aaron Sidford

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aaron Sidford

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

All Works

20 of 20 papers shown
1.
Sidford, Aaron, et al.. (2024). Truncated Variance Reduced Value Iteration. 117481–117508.
2.
Sidford, Aaron, et al.. (2023). Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract). 6468–6473. 1 indexed citations
3.
Sidford, Aaron, Mengdi Wang, Xian Wu, & Yinyu Ye. (2021). Variance reduced value iteration and faster algorithms for solving Markov decision processes. Naval Research Logistics (NRL). 70(5). 423–442. 8 indexed citations
4.
Carmon, Yair, et al.. (2020). Coordinate Methods for Matrix Games. 283–293. 5 indexed citations
5.
Carmon, Yair, et al.. (2019). A Rank-1 Sketch for Matrix Multiplicative Weights. Conference on Learning Theory. 589–623. 2 indexed citations
6.
Sidford, Aaron, Mengdi Wang, Lin F. Yang, & Yinyu Ye. (2019). Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. International Conference on Artificial Intelligence and Statistics. 2992–3002. 2 indexed citations
7.
Bubeck, Sébastien, et al.. (2019). Near-optimal method for highly smooth convex optimization. Conference on Learning Theory. 492–507. 6 indexed citations
8.
Bubeck, Sébastien, et al.. (2019). Complexity of Highly Parallel Non-Smooth Convex Optimization. Neural Information Processing Systems. 32. 13900–13909. 1 indexed citations
9.
Carmon, Yair, et al.. (2019). Variance Reduction for Matrix Games. Neural Information Processing Systems. 32. 11381–11392. 5 indexed citations
10.
Sidford, Aaron, Mengdi Wang, Xian Wu, Lin F. Yang, & Yinyu Ye. (2018). Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. neural information processing systems. 31. 5186–5196. 26 indexed citations
11.
Jain, Prateek, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, & Aaron Sidford. (2018). Accelerating Stochastic Gradient Descent for Least Squares Regression. Conference on Learning Theory. 545–604. 5 indexed citations
12.
Sidford, Aaron, et al.. (2018). Coordinate Methods for Accelerating ℓ∞ Regression and Faster Approximate Maximum Flow. 922–933. 9 indexed citations
13.
Jain, Prateek, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, & Aaron Sidford. (2017). Accelerating Stochastic Gradient Descent. arXiv (Cornell University). 10 indexed citations
14.
Jain, Prateek, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, & Aaron Sidford. (2016). Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm.. arXiv (Cornell University). 2 indexed citations
15.
Jain, Prateek, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, & Aaron Sidford. (2016). Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging.. arXiv (Cornell University). 4 indexed citations
16.
Garber, Dan, Elad Hazan, Chi Jin, et al.. (2016). Faster eigenvector computation via shift-and-invert preconditioning. 2626–2634. 2 indexed citations
17.
Cohen, Michael B., Yin Tat Lee, Gary L. Miller, Jakub Pachocki, & Aaron Sidford. (2016). Geometric median in nearly linear time. 9–21. 53 indexed citations
18.
Frostig, Roy, Rong Ge, Sham M. Kakade, & Aaron Sidford. (2015). Competing with the Empirical Risk Minimizer in a Single Pass. Conference on Learning Theory. 728–763. 24 indexed citations
19.
Kapralov, Michael, Yin Tat Lee, Cameron Musco, Christopher Musco, & Aaron Sidford. (2014). Single Pass Spectral Sparsification in Dynamic Streams. 561–570. 25 indexed citations
20.
Kelner, Jonathan A., Yin Tat Lee, Lorenzo Orecchia, & Aaron Sidford. (2013). An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. DSpace@MIT (Massachusetts Institute of Technology). 217–226. 55 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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