Alex Gittens
Impact in
- Computational Mathematics top 5%
- Tensor decomposition and applications
- Statistics and Probability top 5%
- Statistical Methods and Inference
- Random Matrices and Applications
Papers in
-
- Stochastic Gradient Optimization Techniques 5
- Advanced Graph Neural Networks 3
- Domain Adaptation and Few-Shot Learning 3
- Adversarial Robustness in Machine Learning 2
-
- Sparse and Compressive Sensing Techniques 10
- Co-authors
- Michael W. Mahoney (7 shared papers)Christos Boutsidis (2 shared papers)Shusen Wang (2 shared papers)Dimitris Achlioptas (1 shared paper)Joel A. Tropp (1 shared paper)Dennis DeCoste (1 shared paper)Roszilah Hamid (1 shared paper)Ying Xiao (1 shared paper)
- Journals
- Journal of Machine Learning Research (2 papers)Journal of Materials Chemistry (1 paper)Information and Inference A Journal of the IMA (1 paper)International Journal of Computer Vision (1 paper)IEEE Access (1 paper)
- Partner nations
- United StatesPuerto RicoChina
In The Last Decade
Alex Gittens
22 papers receiving 326 citations
Peers
Comparison fields: 5 of 71
- Computational Mathematics 22
- Statistics and Probability 51
- Artificial Intelligence 195
- Computational Mechanics 106
- Computer Vision and Pattern Recognition 102
Countries citing papers authored by Alex Gittens
This map shows the geographic impact of Alex Gittens'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 Alex Gittens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex Gittens more than expected).
Fields of papers citing papers by Alex Gittens
This network shows the impact of papers produced by Alex Gittens. 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 Alex Gittens. The network helps show where Alex Gittens may publish in the future.
Co-authors
The 25 scholars most cited alongside Alex Gittens, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 58 | |
| 2 | 2013 | 38 | |
| 3 | Scalable kernel k-means clustering with Nyström approximation: relative-error bounds | 2019 | 32 |
| 4 | 2017 | 31 | |
| 5 | Compact Random Feature Maps | 2014 | 27 |
| 6 | 2016 | 26 | |
| 7 | 2018 | 24 | |
| 8 | 2012 | 23 | |
| 9 | 2017 | 23 | |
| 10 | 2003 | 16 | |
| 11 | Approximate Spectral Clustering via Randomized Sketching | 2013 | 10 |
| 12 | 2009 | 7 | |
| 13 | 2022 | 6 | |
| 14 | 2022 | 4 | |
| 15 | Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition | 2020 | 4 |
| 16 | 2022 | 3 | |
| 17 | 2023 | 3 | |
| 18 | 2016 | 3 | |
| 19 | 2018 | 2 | |
| 20 | 2023 | 1 |
About Alex Gittens
Alex Gittens is a scholar working on Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Statistics and Probability, having authored 23 papers that have together received 343 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (10 papers), Stochastic Gradient Optimization Techniques (5 papers), Face and Expression Recognition (4 papers), Advanced Graph Neural Networks (3 papers), Matrix Theory and Algorithms (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Adversarial Robustness in Machine Learning (2 papers) and Tensor decomposition and applications (2 papers). The work is most often cited by research in Computational Mathematics (22 citations), Statistics and Probability (51 citations), Artificial Intelligence (195 citations), Computational Mechanics (106 citations) and Computer Vision and Pattern Recognition (102 citations). Alex Gittens has collaborated with scholars based in United States, Puerto Rico and China. Frequent co-authors include Michael W. Mahoney, Christos Boutsidis, Shusen Wang, Dimitris Achlioptas, Joel A. Tropp, Dennis DeCoste, Roszilah Hamid, Ying Xiao, Liping Jing and Bo Liu. Their work appears in journals such as Journal of Machine Learning Research, Journal of Materials Chemistry, Information and Inference A Journal of the IMA, International Journal of Computer Vision and IEEE Access.
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.