Eyal Even-Dar

2.9k citations
22 papers · 767 · h-index 14

Impact in

Papers in

Eyal Even-Dar

22 papers receiving 723 citations

Peers

Eyal Even-Dar
Comparison fields: 5 of 53
  • Management Science and Operations Research 519
  • Computer Networks and Communications 235
  • Statistical and Nonlinear Physics 126
  • Artificial Intelligence 299
  • Economics and Econometrics 147
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Citations per field
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Citations per year

Countries citing papers authored by Eyal Even-Dar

Since Specialization
Citations

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

Fields of papers citing papers by Eyal Even-Dar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eyal Even-Dar. 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 Eyal Even-Dar. The network helps show where Eyal Even-Dar may publish in the future.

Co-authors

The 25 scholars most cited alongside Eyal Even-Dar, 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 Eyal Even-Dar Line = papers co-authored together Eyal Even-Dar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
2006148
2 200787
3 200673
4 200672
5 200957
6 200551
7 201047
8 200939
9 200632
10
Experts in a Markov Decision Process
200431
11
Convergence of Optimistic and Incremental Q-Learning
200123
12
Reinforcement learning in POMDPs without resets
200521
13
201014
14
Regret Minimization with Concept Drift
201014
15
Action elimination and stopping conditions for reinforcement learning
200312
16
Online Learning with Global Cost Functions
200912
17 201411
18 200810
19
The value of observation for monitoring dynamic systems
20076
20 20084

About Eyal Even-Dar

Eyal Even-Dar is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Networks and Communications, Economics and Econometrics and Information Systems, having authored 22 papers that have together received 767 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (10 papers), Optimization and Search Problems (9 papers), Game Theory and Applications (8 papers), Economic theories and models (6 papers), Reinforcement Learning in Robotics (6 papers), Machine Learning and Algorithms (5 papers), Game Theory and Voting Systems (2 papers) and Opinion Dynamics and Social Influence (2 papers). The work is most often cited by research in Management Science and Operations Research (519 citations), Computer Networks and Communications (235 citations), Statistical and Nonlinear Physics (126 citations), Artificial Intelligence (299 citations) and Economics and Econometrics (147 citations). Eyal Even-Dar has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Yishay Mansour, Shie Mannor, Yishay Mansour, Sham M. Kakade, Avrim Blum, Alex Kesselman, Katrina Ligett, A. Shapira, Liam Roditty and Susanne Albers. Their work appears in journals such as Theory of Computing, Machine Learning, ACM Transactions on Algorithms, Mathematics of Operations Research and Journal of Machine Learning Research.

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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