Aviral Kumar
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
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- Advanced Bandit Algorithms Research
Papers in
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- Reinforcement Learning in Robotics 8
- Adversarial Robustness in Machine Learning 4
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- Fuel Cells and Related Materials 1
- Co-authors
- Sunita Sarawagi (1 shared paper)Sergey Levine (11 shared papers)George Tucker (3 shared papers)Justin Fu (3 shared papers)Ed H. (1 shared paper)Minmin Chen (1 shared paper)Can Xu (1 shared paper)Anikait Singh (2 shared papers)
- Journals
- Industrial & Engineering Chemistry Research (1 paper)Human Brain Mapping (1 paper)Journal of Neuroscience (1 paper)arXiv (Cornell University) (6 papers)International Conference on Learning Representations (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Aviral Kumar
16 papers receiving 141 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 98
- Management Science and Operations Research 28
- Computer Vision and Pattern Recognition 33
- Control and Systems Engineering 26
- Information Systems 21
Countries citing papers authored by Aviral Kumar
This map shows the geographic impact of Aviral Kumar'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 Aviral Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aviral Kumar more than expected).
Fields of papers citing papers by Aviral Kumar
This network shows the impact of papers produced by Aviral Kumar. 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 Aviral Kumar. The network helps show where Aviral Kumar may publish in the future.
Co-authors
The 25 scholars most cited alongside Aviral Kumar, 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 | Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings | 2018 | 41 |
| 2 | Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction | 2019 | 30 |
| 3 | 2022 | 22 | |
| 4 | 2023 | 18 | |
| 5 | Graph Normalizing Flows | 2019 | 9 |
| 6 | Conservative Q-Learning for Offline Reinforcement Learning | 2020 | 5 |
| 7 | Conservative Safety Critics for Exploration | 2021 | 4 |
| 8 | 2022 | 4 | |
| 9 | 2019 | 4 | |
| 10 | 2022 | 3 | |
| 11 | Datasets for Data-Driven Reinforcement Learning | 2020 | 2 |
| 12 | 2023 | 2 | |
| 13 | 2024 | 2 | |
| 14 | 2024 | 2 | |
| 15 | 2024 | 1 | |
| 16 | 2025 | 1 | |
| 17 | 2025 | 0 | |
| 18 | 2024 | 0 |
About Aviral Kumar
Aviral Kumar is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Control and Systems Engineering, Cognitive Neuroscience and Computational Theory and Mathematics, having authored 18 papers that have together received 150 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Adversarial Robustness in Machine Learning (4 papers), Robot Manipulation and Learning (2 papers), Functional Brain Connectivity Studies (2 papers), Autonomous Vehicle Technology and Safety (1 paper), Catalytic Processes in Materials Science (1 paper), Fuel Cells and Related Materials (1 paper) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Artificial Intelligence (98 citations), Management Science and Operations Research (28 citations), Computer Vision and Pattern Recognition (33 citations), Control and Systems Engineering (26 citations) and Information Systems (21 citations). Aviral Kumar has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Sunita Sarawagi, Sergey Levine, George Tucker, Justin Fu, Ed H., Minmin Chen, Can Xu, Anikait Singh, Chelsea Finn and Frederik Ebert. Their work appears in journals such as Industrial & Engineering Chemistry Research, Human Brain Mapping, Journal of Neuroscience, arXiv (Cornell University) and International Conference on Learning Representations.
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.