Aviral Kumar

4.0k total citations
17 papers, 146 citations indexed

About

Aviral Kumar is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Control and Systems Engineering. According to data from OpenAlex, Aviral Kumar has authored 17 papers receiving a total of 146 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 3 papers in Electrical and Electronic Engineering and 2 papers in Control and Systems Engineering. Recurrent topics in Aviral Kumar's work include Reinforcement Learning in Robotics (8 papers), Adversarial Robustness in Machine Learning (4 papers) and Robot Manipulation and Learning (2 papers). Aviral Kumar is often cited by papers focused on Reinforcement Learning in Robotics (8 papers), Adversarial Robustness in Machine Learning (4 papers) and Robot Manipulation and Learning (2 papers). Aviral Kumar collaborates with scholars based in United States, Canada and United Kingdom. Aviral Kumar's co-authors include Sunita Sarawagi, Sergey Levine, George Tucker, Justin Fu, Minmin Chen, Ed H., Can Xu, Frederik Ebert, Chelsea Finn and Anikait Singh and has published in prestigious journals such as Industrial & Engineering Chemistry Research, Human Brain Mapping and arXiv (Cornell University).

In The Last Decade

Aviral Kumar

15 papers receiving 137 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Aviral Kumar United States	6	96	33	28	26	21	17	146
Zain Ul Abideen Pakistan	8	61 0.6×	27 0.8×	11 0.4×	8 0.3×	24 1.1×	22	155
Amir-Hossein Karimi Germany	5	143 1.5×	9 0.3×	9 0.3×	46 1.8×	13 0.6×	7	222
Yutong Dai China	5	132 1.4×	23 0.7×	9 0.3×	4 0.2×	30 1.4×	14	204
Matthew Riemer United States	6	122 1.3×	24 0.7×	25 0.9×	29 1.1×	10 0.5×	13	187
Chenhao Xie China	6	131 1.4×	57 1.7×	16 0.6×	5 0.2×	30 1.4×	14	180
Meng Liao China	7	190 2.0×	33 1.0×	22 0.8×	39 1.5×	26 1.2×	14	261
Noreddine Gherabi Morocco	9	80 0.8×	5 0.2×	20 0.7×	17 0.7×	36 1.7×	48	177
Yulong Chen China	10	237 2.5×	48 1.5×	8 0.3×	7 0.3×	24 1.1×	25	328
Rahul Iyer United States	6	128 1.3×	17 0.5×	9 0.3×	16 0.6×	14 0.7×	13	188
Sertan Girgin United States	7	112 1.2×	9 0.3×	13 0.5×	9 0.3×	7 0.3×	17	153

Countries citing papers authored by Aviral Kumar

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Aviral Kumar

This figure shows the co-authorship network connecting the top 25 collaborators of Aviral Kumar. A scholar is included among the top collaborators of Aviral Kumar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aviral Kumar. Aviral Kumar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Wallace, Eric, et al.. (2025). Unfamiliar Finetuning Examples Control How Language Models Hallucinate. 3600–3612. 1 indexed citations

Singh, Anikait, et al.. (2024). Robotic Offline RL from Internet Videos via Value-Function Learning. 16977–16984. 2 indexed citations

Kumar, Aviral, et al.. (2024). Recursive Introspection: Teaching Language Model Agents How to Self-Improve. 55249–55285. 1 indexed citations

Kumar, Aviral, et al.. (2024). Is Value Learning Really the Main Bottleneck in Offline RL?. 79029–79056.

Bai, Hao, et al.. (2024). DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning. 12461–12495.

Kumar, Aviral, et al.. (2023). Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials. 18 indexed citations

Kumar, Aviral, et al.. (2023). Computational Study on the Effect of Thermal Boundary Conditions and Axial Aspect Ratio on Catalytic Oxidative Coupling of Methane. Industrial & Engineering Chemistry Research. 62(46). 19907–19919. 2 indexed citations

Bero, J. J., Yang Li, Aviral Kumar, et al.. (2022). Coordinated anatomical and functional variability in the human brain during adolescence. Human Brain Mapping. 44(4). 1767–1778. 4 indexed citations

Geng, Xinyang, et al.. (2022). Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization. arXiv (Cornell University). 3 indexed citations

10.

Chen, Minmin, et al.. (2022). Off-Policy Actor-critic for Recommender Systems. 338–349. 22 indexed citations

11.

Bharadhwaj, Homanga, Aviral Kumar, Nicholas Rhinehart, et al.. (2021). Conservative Safety Critics for Exploration. International Conference on Learning Representations. 4 indexed citations

12.

Fu, Justin, Aviral Kumar, Ofir Nachum, George Tucker, & Sergey Levine. (2020). Datasets for Data-Driven Reinforcement Learning. arXiv (Cornell University). 2 indexed citations

13.

Kumar, Aviral, Aurick Zhou, George Tucker, & Sergey Levine. (2020). Conservative Q-Learning for Offline Reinforcement Learning. arXiv (Cornell University). 33. 1179–1191. 5 indexed citations

14.

Liu, Jenny, Aviral Kumar, Jimmy Ba, Jamie Kiros, & Kevin Swersky. (2019). Graph Normalizing Flows. arXiv (Cornell University). 32. 13556–13566. 9 indexed citations

15.

Kumar, Aviral, et al.. (2019). Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. arXiv (Cornell University). 32. 11761–11771. 30 indexed citations

16.

Fu, Justin, et al.. (2019). Diagnosing Bottlenecks in Deep Q-learning Algorithms. arXiv (Cornell University). 2021–2030. 4 indexed citations

17.

Kumar, Aviral, et al.. (2018). Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings. International Conference on Machine Learning. 2805–2814. 39 indexed citations

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.

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