Sanmit Narvekar

801 total citations
11 papers, 232 citations indexed

About

Sanmit Narvekar is a scholar working on Artificial Intelligence, Control and Systems Engineering and Information Systems. According to data from OpenAlex, Sanmit Narvekar has authored 11 papers receiving a total of 232 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Control and Systems Engineering and 3 papers in Information Systems. Recurrent topics in Sanmit Narvekar's work include Reinforcement Learning in Robotics (8 papers), Evolutionary Algorithms and Applications (6 papers) and Robot Manipulation and Learning (5 papers). Sanmit Narvekar is often cited by papers focused on Reinforcement Learning in Robotics (8 papers), Evolutionary Algorithms and Applications (6 papers) and Robot Manipulation and Learning (5 papers). Sanmit Narvekar collaborates with scholars based in United States. Sanmit Narvekar's co-authors include Peter Stone, Jivko Sinapov, Matteo Leonetti, Tushar Chandra, Eugene Ie, Heng-Tze Cheng, Craig Boutilier, Jing Wang, Rui Wu and Vihan Jain and has published in prestigious journals such as Journal of Machine Learning Research, IEEE Intelligent Systems and arXiv (Cornell University).

In The Last Decade

Sanmit Narvekar

10 papers receiving 223 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sanmit Narvekar United States	7	168	58	53	46	32	11	232
Lisa Torrey United States	7	189 1.1×	15 0.3×	23 0.4×	67 1.5×	24 0.8×	15	264
Abdalraouf Hassan United States	5	295 1.8×	65 1.1×	30 0.6×	10 0.2×	33 1.0×	8	375
Lanxiao Huang United States	5	153 0.9×	25 0.4×	18 0.3×	25 0.5×	44 1.4×	7	257
Brian Carse United Kingdom	9	312 1.9×	26 0.4×	25 0.5×	73 1.6×	30 0.9×	39	392
Matthew Riemer United States	6	122 0.7×	10 0.2×	25 0.5×	29 0.6×	24 0.8×	13	187
Alexandros Agapitos Ireland	10	196 1.2×	18 0.3×	18 0.3×	22 0.5×	28 0.9×	28	255
Akshay Agrawal United States	6	81 0.5×	32 0.6×	15 0.3×	21 0.5×	29 0.9×	16	199
Alberto Maria Metelli Italy	7	109 0.6×	14 0.2×	26 0.5×	43 0.9×	22 0.7×	32	205
Bei Peng United States	8	159 0.9×	11 0.2×	14 0.3×	61 1.3×	21 0.7×	18	199
Toryn Q. Klassen Canada	7	280 1.7×	24 0.4×	15 0.3×	51 1.1×	32 1.0×	14	334

Countries citing papers authored by Sanmit Narvekar

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Sanmit Narvekar

This figure shows the co-authorship network connecting the top 25 collaborators of Sanmit Narvekar. A scholar is included among the top collaborators of Sanmit Narvekar 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 Sanmit Narvekar. Sanmit Narvekar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Mirsky, Reuth, et al.. (2021). Capturing Skill State in Curriculum Learning for Human Skill Acquisition. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 771–776. 5 indexed citations

2.

Narvekar, Sanmit. (2021). Curriculum learning in reinforcement learning. Texas Digital Library (University of Texas).

3.

Narvekar, Sanmit, Bei Peng, Matteo Leonetti, et al.. (2020). Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. Journal of Machine Learning Research. 21(181). 1–50. 18 indexed citations

4.

Narvekar, Sanmit & Peter Stone. (2020). Generalizing Curricula for Reinforcement Learning. 1 indexed citations

5.

Narvekar, Sanmit & Peter Stone. (2019). Learning Curriculum Policies for Reinforcement Learning. arXiv (Cornell University). 25–33. 17 indexed citations

6.

Ie, Eugene, Vihan Jain, Jing Wang, et al.. (2019). SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets. 2592–2599. 66 indexed citations

7.

Narvekar, Sanmit, Jivko Sinapov, & Peter Stone. (2017). Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning. 2536–2542. 50 indexed citations

8.

Narvekar, Sanmit. (2017). Curriculum Learning in Reinforcement Learning. 5195–5196. 20 indexed citations

9.

Narvekar, Sanmit, Jivko Sinapov, Matteo Leonetti, & Peter Stone. (2016). Source Task Creation for Curriculum Learning. Adaptive Agents and Multi-Agents Systems. 566–574. 36 indexed citations

10.

MacAlpine, Patrick, et al.. (2016). UT Austin Villa: Project-Driven Research in AI and Robotics. IEEE Intelligent Systems. 31(2). 94–101. 2 indexed citations

11.

Sinapov, Jivko, Sanmit Narvekar, Matteo Leonetti, & Peter Stone. (2015). Learning Inter-Task Transferability in the Absence of Target Task Samples. Adaptive Agents and Multi-Agents Systems. 725–733. 17 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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