Aaron Sidford
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- Complexity and Algorithms in Graphs 5
- Matrix Theory and Algorithms 2
- Artificial Intelligence top 10%
- Stochastic Gradient Optimization Techniques 14
- Machine Learning and Algorithms 5
- Reinforcement Learning in Robotics 3
- Neural Networks and Applications 1
- Statistics and Probability top 10%
- Markov Chains and Monte Carlo Methods 2
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- Sparse and Compressive Sensing Techniques 7
Aaron Sidford
19 papers receiving 229 citations
Peers
Comparison fields: 5 of 49
- Computational Mathematics 5
- Computational Theory and Mathematics 90
- Artificial Intelligence 146
- Statistics and Probability 30
- Numerical Analysis 18
Countries citing papers authored by Aaron Sidford
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
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
The 25 scholars most cited alongside Aaron Sidford, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2023 | 1 | |
| 3 | 2021 | 8 | |
| 4 | 2020 | 5 | |
| 5 | A Rank-1 Sketch for Matrix Multiplicative Weights | 2019 | 2 |
| 6 | Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity | 2019 | 2 |
| 7 | Near-optimal method for highly smooth convex optimization | 2019 | 6 |
| 8 | Complexity of Highly Parallel Non-Smooth Convex Optimization | 2019 | 1 |
| 9 | Variance Reduction for Matrix Games | 2019 | 5 |
| 10 | Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model | 2018 | 26 |
| 11 | Accelerating Stochastic Gradient Descent for Least Squares Regression | 2018 | 5 |
| 12 | 2018 | 9 | |
| 13 | Accelerating Stochastic Gradient Descent | 2017 | 10 |
| 14 | Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. | 2016 | 2 |
| 15 | Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. | 2016 | 4 |
| 16 | Faster eigenvector computation via shift-and-invert preconditioning | 2016 | 2 |
| 17 | 2016 | 53 | |
| 18 | Competing with the Empirical Risk Minimizer in a Single Pass | 2015 | 24 |
| 19 | 2014 | 25 | |
| 20 | 2013 | 55 |
About Aaron Sidford
Aaron Sidford is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computational Mechanics, having authored 20 papers that have together received 245 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (14 papers), Sparse and Compressive Sensing Techniques (7 papers), Machine Learning and Algorithms (5 papers), Complexity and Algorithms in Graphs (5 papers), Reinforcement Learning in Robotics (3 papers), Markov Chains and Monte Carlo Methods (2 papers), Matrix Theory and Algorithms (2 papers) and Neural Networks and Applications (1 paper). The work is most often cited by research in Computational Mathematics (5 citations), Computational Theory and Mathematics (90 citations) and Artificial Intelligence (146 citations). Aaron Sidford has collaborated with scholars based in United States and United Kingdom. Frequent 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.
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.