Yonathan Efroni

467 citations
10 papers · 82 · h-index 6

Impact in

Papers in

Journals
Physical Review Letters (1 paper)International Conference on Machine Learning (1 paper)Neural Information Processing Systems (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (3 papers)

In The Last Decade

Yonathan Efroni

10 papers receiving 76 citations

Peers

Yonathan Efroni
Comparison fields: 5 of 31
  • Management Science and Operations Research 26
  • Artificial Intelligence 61
  • Computational Theory and Mathematics 11
  • Control and Systems Engineering 10
  • Computer Vision and Pattern Recognition 8
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Countries citing papers authored by Yonathan Efroni

Since Specialization
Citations

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

Fields of papers citing papers by Yonathan Efroni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 8 scholars most cited alongside Yonathan Efroni, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yonathan Efroni Line = papers co-authored together Yonathan Efroni links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 202038
2
Action Robust Reinforcement Learning and Applications in Continuous Control
20199
3 20219
4 20177
5 20195
6
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
20185
7 20183
8
Optimistic Policy Optimization with Bandit Feedback
20203
9
Multi-Step Greedy and Approximate Real Time Dynamic Programming
20192
10
Revisiting Exploration-Conscious Reinforcement Learning.
20181

About Yonathan Efroni

Yonathan Efroni is a scholar working on Artificial Intelligence, Management Science and Operations Research, Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics and Materials Chemistry, having authored 10 papers that have together received 82 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Advanced Bandit Algorithms Research (4 papers), Auction Theory and Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers), Evolutionary Algorithms and Applications (1 paper), Machine Learning and Algorithms (1 paper), Topological Materials and Phenomena (1 paper) and Graphene research and applications (1 paper). The work is most often cited by research in Management Science and Operations Research (26 citations), Artificial Intelligence (61 citations), Computational Theory and Mathematics (11 citations), Control and Systems Engineering (10 citations) and Computer Vision and Pattern Recognition (8 citations). Yonathan Efroni has collaborated with scholars based in Israel, France and United States. Frequent co-authors include Shie Mannor, Lior Shani, Bruno Scherrer, Chen Tessler, Gal Dalal, Shahal Ilani, Erez Berg and Mohammad Ghavamzadeh. Their work appears in journals such as Physical Review Letters, International Conference on Machine Learning, Neural Information Processing Systems, Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).

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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