Rishabh Agarwal
Impact in
-
- Autonomous Vehicle Technology and Safety
-
- Artificial Intelligence in Healthcare and Education
Papers in
-
- Reinforcement Learning in Robotics 4
- Topic Modeling 1
- Adversarial Robustness in Machine Learning 1
- Domain Adaptation and Few-Shot Learning 1
-
- Advanced Electron Microscopy Techniques and Applications 1
- Co-authors
- Mohammad Norouzi (2 shared papers)Dale Schuurmans (1 shared paper)Pierre‐Luc Bacon (1 shared paper)Aaron C. Courville (2 shared papers)Pablo Samuel Castro (2 shared papers)Marc G. Bellemare (2 shared papers)Gabriel Dulac-Arnold (1 shared paper)Sergei V. Kalinin (1 shared paper)
- Journals
- Microscopy and Microanalysis (1 paper)International Conference on Machine Learning (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)neural information processing systems (5 papers)
- Partner nations
- United StatesCanadaChina
In The Last Decade
Rishabh Agarwal
7 papers receiving 61 citations
Peers
Comparison fields: 5 of 24
- Automotive Engineering 19
- Health Informatics 3
- Artificial Intelligence 29
- Computer Vision and Pattern Recognition 6
- Control and Systems Engineering 5
Countries citing papers authored by Rishabh Agarwal
This map shows the geographic impact of Rishabh Agarwal'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 Rishabh Agarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rishabh Agarwal more than expected).
Fields of papers citing papers by Rishabh Agarwal
This network shows the impact of papers produced by Rishabh Agarwal. 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 Rishabh Agarwal. The network helps show where Rishabh Agarwal may publish in the future.
Co-authors
The 25 scholars most cited alongside Rishabh Agarwal, 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 | 2023 | 23 | |
| 2 | An Optimistic Perspective on Offline Deep Reinforcement Learning | 2020 | 13 |
| 3 | 2022 | 7 | |
| 4 | 2024 | 7 | |
| 5 | 2022 | 5 | |
| 6 | RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. | 2020 | 3 |
| 7 | 2024 | 3 | |
| 8 | 2023 | 0 |
About Rishabh Agarwal
Rishabh Agarwal is a scholar working on Artificial Intelligence, Structural Biology, Automotive Engineering, Safety Research and Nuclear Energy and Engineering, having authored 8 papers that have together received 61 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Ethics and Social Impacts of AI (1 paper), Topic Modeling (1 paper), Advanced Electron Microscopy Techniques and Applications (1 paper), Adversarial Robustness in Machine Learning (1 paper), Autonomous Vehicle Technology and Safety (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Automotive Engineering (19 citations), Health Informatics (3 citations), Artificial Intelligence (29 citations), Computer Vision and Pattern Recognition (6 citations) and Control and Systems Engineering (5 citations). Rishabh Agarwal has collaborated with scholars based in United States, Canada and China. Frequent co-authors include Mohammad Norouzi, Dale Schuurmans, Pierre‐Luc Bacon, Aaron C. Courville, Pablo Samuel Castro, Marc G. Bellemare, Gabriel Dulac-Arnold, Sergei V. Kalinin, Çağlar Gülçehre and János Kramár. Their work appears in journals such as Microscopy and Microanalysis, International Conference on Machine Learning, Proceedings of the AAAI Conference on Artificial Intelligence and neural information processing systems.
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.