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
- Data Stream Mining Techniques 1
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- Advancements in Semiconductor Devices and Circuit Design 1
- Co-authors
- Sergey Levine (11 shared papers)Sunita Sarawagi (1 shared paper)George Tucker (3 shared papers)Justin Fu (3 shared papers)Can Xu (1 shared paper)Minmin Chen (1 shared paper)Ed H. (1 shared paper)Chelsea Finn (1 shared paper)
- Journals
- Industrial & Engineering Chemistry Research (1 paper)Human Brain Mapping (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (6 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Aviral Kumar
15 papers receiving 137 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 96
- 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 | 39 |
| 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 | 2022 | 4 | |
| 8 | Conservative Safety Critics for Exploration | 2021 | 4 |
| 9 | 2019 | 4 | |
| 10 | 2022 | 3 | |
| 11 | Datasets for Data-Driven Reinforcement Learning | 2020 | 2 |
| 12 | 2024 | 2 | |
| 13 | 2023 | 2 | |
| 14 | 2025 | 1 | |
| 15 | 2024 | 1 | |
| 16 | 2024 | 0 | |
| 17 | 2024 | 0 |
About Aviral Kumar
Aviral Kumar is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Control and Systems Engineering, Computational Theory and Mathematics and Automotive Engineering, having authored 17 papers that have together received 146 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), Data Stream Mining Techniques (1 paper), Advanced Neuroimaging Techniques and Applications (1 paper), Advancements in Semiconductor Devices and Circuit Design (1 paper), Mechanical Circulatory Support Devices (1 paper) and Catalysis and Oxidation Reactions (1 paper). The work is most often cited by research in Artificial Intelligence (96 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, Canada and United Kingdom. Frequent co-authors include Sergey Levine, Sunita Sarawagi, George Tucker, Justin Fu, Can Xu, Minmin Chen, Ed H., Chelsea Finn, Anikait Singh and Frederik Ebert. Their work appears in journals such as Industrial & Engineering Chemistry Research, Human Brain Mapping, International Conference on Learning Representations, arXiv (Cornell University) and International Conference on Machine Learning.
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.