Nir Levine

2.0k total citations · 1 hit paper
7 papers, 367 citations indexed

About

Nir Levine is a scholar working on Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, Nir Levine has authored 7 papers receiving a total of 367 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 1 paper in Computer Networks and Communications and 1 paper in Control and Systems Engineering. Recurrent topics in Nir Levine's work include Reinforcement Learning in Robotics (3 papers), Statistical and numerical algorithms (1 paper) and Information Retrieval and Search Behavior (1 paper). Nir Levine is often cited by papers focused on Reinforcement Learning in Robotics (3 papers), Statistical and numerical algorithms (1 paper) and Information Retrieval and Search Behavior (1 paper). Nir Levine collaborates with scholars based in Israel, United States and United Kingdom. Nir Levine's co-authors include Daniel J. Mankowitz, Jerry Li, Gabriel Dulac-Arnold, Todd Hester, Sven Gowal, Cosmin Păduraru, Jason L. Speyer, Henry J. Kelley, Shie Mannor and WALTER F. DENHAM and has published in prestigious journals such as Automatica, Machine Learning and Bell System Technical Journal.

In The Last Decade

Nir Levine

7 papers receiving 345 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Challenges of real-world reinforcement learning: definitions, benchmarks and analysis

2021 320 citations Gabriel Dulac-Arnold, Nir Levine et al. Machine Learning profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nir Levine Israel	6	156	99	62	51	38	7	367
Er Meng Joo Singapore	10	159 1.0×	106 1.1×	77 1.2×	84 1.6×	36 0.9×	26	385
Tuomas Haarnoja Finland	7	256 1.6×	119 1.2×	69 1.1×	28 0.5×	42 1.1×	19	424
Seung-Hwan Choi South Korea	11	109 0.7×	72 0.7×	79 1.3×	46 0.9×	43 1.1×	42	373
Sven Gronauer Germany	3	190 1.2×	84 0.8×	77 1.2×	115 2.3×	41 1.1×	6	435
Daniel J. Mankowitz Israel	8	301 1.9×	127 1.3×	78 1.3×	68 1.3×	30 0.8×	19	540
Roland Hafner Germany	6	266 1.7×	144 1.5×	38 0.6×	27 0.5×	23 0.6×	15	410
Borja Fernandez‐Gauna Spain	11	104 0.7×	78 0.8×	67 1.1×	66 1.3×	72 1.9×	29	303
Shayegan Omidshafiei United States	10	229 1.5×	86 0.9×	26 0.4×	91 1.8×	45 1.2×	31	403
Yongzhi Wang China	11	177 1.1×	55 0.6×	41 0.7×	122 2.4×	36 0.9×	54	419
Seyede Fatemeh Ghoreishi United States	13	149 1.0×	120 1.2×	41 0.7×	54 1.1×	18 0.5×	32	471

Countries citing papers authored by Nir Levine

Since Specialization

Citations

This map shows the geographic impact of Nir Levine'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 Nir Levine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nir Levine more than expected).

Fields of papers citing papers by Nir Levine

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nir Levine. 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 Nir Levine. The network helps show where Nir Levine may publish in the future.

Co-authorship network of co-authors of Nir Levine

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

All Works

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7 of 7 papers shown

1.

Dulac-Arnold, Gabriel, Nir Levine, Daniel J. Mankowitz, et al.. (2021). Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Machine Learning. 110(9). 2419–2468. 320 indexed citations breakdown →

2.

Mankowitz, Daniel J., Nir Levine, Abbas Abdolmaleki, et al.. (2020). Robust Reinforcement Learning for Continuous Control with Model Misspecification. arXiv (Cornell University). 1 indexed citations

3.

Levine, Nir, Koby Crammer, & Shie Mannor. (2017). Rotting Bandits. Neural Information Processing Systems. 30. 3074–3083. 6 indexed citations

4.

Levine, Nir, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, & Shie Mannor. (2017). Shallow Updates for Deep Reinforcement Learning. Neural Information Processing Systems. 30. 3135–3145. 5 indexed citations

5.

Levine, Nir, Haggai Roitman, & Doron Cohen. (2017). An Extended Relevance Model for Session Search. 865–868. 9 indexed citations

6.

Speyer, Jason L., Henry J. Kelley, Nir Levine, & WALTER F. DENHAM. (1971). Accelerated gradient projection technique with application to rocket trajectory optimization. Automatica. 7(1). 37–43. 12 indexed citations

7.

Levine, Nir. (1961). A New Technique for Increasing the Flexibility of Recursive Least Squares Data Smoothing. Bell System Technical Journal. 40(3). 821–840. 14 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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