Nishant A. Mehta
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- Machine Learning and Algorithms 6
- Machine Learning and Data Classification 2
- Neural Networks and Applications 2
- Domain Adaptation and Few-Shot Learning 2
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- Statistical Methods and Inference 3
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- EEG and Brain-Computer Interfaces 3
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- Advanced Bandit Algorithms Research 3
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- Sparse and Compressive Sensing Techniques 2
- Co-authors
- Alexander GrayPeter GrünwaldParikshit RamRyan R. CurtinRobert C. WilliamsonMark D. ReidTim van ErvenDonald L. Bliwise
- Journals
- Journal of Machine Learning Research (3 papers)Biotechnology and Bioengineering (1 paper)Biomedical Signal Processing and Control (1 paper)
- Partner nations
- United StatesCanadaNetherlands
In The Last Decade
Nishant A. Mehta
15 papers receiving 174 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 84
- Statistics and Probability 20
- Computational Mathematics 1
- Hardware and Architecture 11
- Computer Vision and Pattern Recognition 32
Countries citing papers authored by Nishant A. Mehta
This map shows the geographic impact of Nishant A. Mehta'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 Nishant A. Mehta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nishant A. Mehta more than expected).
Fields of papers citing papers by Nishant A. Mehta
This network shows the impact of papers produced by Nishant A. Mehta. 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 Nishant A. Mehta. The network helps show where Nishant A. Mehta may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Nishant A. Mehta, 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 | Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes | 2020 | 23 |
| 3 | Safe-Bayesian Generalized Linear Regression | 2020 | 1 |
| 4 | Problem-dependent Regret Bounds for Online Learning with Feedback Graphs. | 2019 | 2 |
| 5 | 2019 | 9 | |
| 6 | 2018 | 5 | |
| 7 | 2015 | 18 | |
| 8 | 2014 | 3 | |
| 9 | 2014 | 1 | |
| 10 | Sparsity-Based Generalization Bounds for Predictive Sparse Coding | 2013 | 17 |
| 11 | 2012 | 13 | |
| 12 | 2012 | 77 | |
| 13 | 2012 | 2 | |
| 14 | 2010 | 4 | |
| 15 | 2010 | 5 | |
| 16 | 2009 | 1 |
About Nishant A. Mehta
Nishant A. Mehta is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 16 papers that have together received 181 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (6 papers), Statistical Methods and Inference (3 papers), EEG and Brain-Computer Interfaces (3 papers), Advanced Bandit Algorithms Research (3 papers), Sparse and Compressive Sensing Techniques (2 papers), Machine Learning and Data Classification (2 papers), Neural Networks and Applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Artificial Intelligence (84 citations), Statistics and Probability (20 citations) and Computational Mathematics (1 citation). Nishant A. Mehta has collaborated with scholars based in United States, Canada and Netherlands. Frequent co-authors include Alexander Gray, Peter Grünwald, Parikshit Ram, Ryan R. Curtin, Robert C. Williamson, Mark D. Reid, Tim van Erven, Donald L. Bliwise, George Georgoulas and Zhiming Huang. Their work appears in journals such as Journal of Machine Learning Research, Biotechnology and Bioengineering, Biomedical Signal Processing and Control, International Journal of Human-Computer Interaction and ANU Open Research (Australian National 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.