Sanmit Narvekar
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
- Data Stream Mining Techniques
- Domain Adaptation and Few-Shot Learning
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- Advanced Bandit Algorithms Research
Papers in
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- Reinforcement Learning in Robotics 9
- Evolutionary Algorithms and Applications 7
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- Robot Manipulation and Learning 6
- Co-authors
- Peter Stone (10 shared papers)Jivko Sinapov (4 shared papers)Matteo Leonetti (3 shared papers)Eugene Ie (1 shared paper)Tushar Chandra (1 shared paper)Rui Wu (1 shared paper)Vihan Jain (1 shared paper)Jing Wang (1 shared paper)
- Journals
- IEEE Intelligent Systems (1 paper)Journal of Machine Learning Research (1 paper)Lecture notes in computer science (2 papers)White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (2 papers)2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1 paper)
- Partner nations
- United States
In The Last Decade
Sanmit Narvekar
13 papers receiving 265 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 191
- Management Science and Operations Research 56
- Computer Science Applications 18
- Information Systems 61
- Computer Vision and Pattern Recognition 51
Countries citing papers authored by Sanmit Narvekar
This map shows the geographic impact of Sanmit Narvekar'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 Sanmit Narvekar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanmit Narvekar more than expected).
Fields of papers citing papers by Sanmit Narvekar
This network shows the impact of papers produced by Sanmit Narvekar. 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 Sanmit Narvekar. The network helps show where Sanmit Narvekar may publish in the future.
Co-authors
The 22 scholars most cited alongside Sanmit Narvekar, 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 | 2019 | 71 | |
| 2 | 2017 | 54 | |
| 3 | 2016 | 40 | |
| 4 | 2017 | 22 | |
| 5 | Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey | 2020 | 20 |
| 6 | 2015 | 19 | |
| 7 | 2019 | 18 | |
| 8 | 2018 | 17 | |
| 9 | 2021 | 6 | |
| 10 | 2016 | 3 | |
| 11 | 2023 | 2 | |
| 12 | 2016 | 1 | |
| 13 | Generalizing Curricula for Reinforcement Learning | 2020 | 1 |
| 14 | 2021 | 0 |
About Sanmit Narvekar
Sanmit Narvekar is a scholar working on Artificial Intelligence, Control and Systems Engineering, Information Systems, Computer Science Applications and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 274 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (9 papers), Evolutionary Algorithms and Applications (7 papers), Robot Manipulation and Learning (6 papers), Mobile Crowdsensing and Crowdsourcing (2 papers), Software Engineering Research (2 papers), Advanced Neural Network Applications (2 papers), Autonomous Vehicle Technology and Safety (1 paper) and Industrial Vision Systems and Defect Detection (1 paper). The work is most often cited by research in Artificial Intelligence (191 citations), Management Science and Operations Research (56 citations), Computer Science Applications (18 citations), Information Systems (61 citations) and Computer Vision and Pattern Recognition (51 citations). Sanmit Narvekar has collaborated with scholars based in United States. Frequent co-authors include Peter Stone, Jivko Sinapov, Matteo Leonetti, Eugene Ie, Tushar Chandra, Rui Wu, Vihan Jain, Jing Wang, Craig Boutilier and Ritesh Agarwal. Their work appears in journals such as IEEE Intelligent Systems, Journal of Machine Learning Research, Lecture notes in computer science, White Rose Research Online (University of Leeds, The University of Sheffield, University of York) and 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.