Lev Reyzin

1.9k citations
42 papers · 777 · h-index 12

Impact in

Papers in

Lev Reyzin

38 papers receiving 741 citations

Peers

Lev Reyzin
Comparison fields: 5 of 87
  • Management Science and Operations Research 297
  • Artificial Intelligence 485
  • Computational Mathematics 8
  • Computational Theory and Mathematics 120
  • Computer Networks and Communications 158
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Citations per field
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Citations per year

Countries citing papers authored by Lev Reyzin

Since Specialization
Citations

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

Fields of papers citing papers by Lev Reyzin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Contextual bandits with linear Payoff functions
2011264
2 2006169
3 201341
4 201738
5
Non-Stochastic Bandit Slate Problems
201033
6 200728
7 201020
8 200614
9
Proceedings of the 28th International Conference on Algorithmic Learning Theory
201713
10
Boosting on a Budget: Sampling for Feature-Efficient Prediction
201112
11 201512
12 201111
13 201910
14 201310
15
An Optimal High Probability Algorithm for the Contextual Bandit Problem
201010
16 20139
17 20149
18 20179
19 20159
20 20198

About Lev Reyzin

Lev Reyzin is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications, Management Science and Operations Research and Organizational Behavior and Human Resource Management, having authored 42 papers that have together received 777 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (27 papers), Complexity and Algorithms in Graphs (17 papers), Optimization and Search Problems (10 papers), Machine Learning and Data Classification (10 papers), Algorithms and Data Compression (6 papers), Imbalanced Data Classification Techniques (5 papers), Cryptography and Data Security (4 papers) and Facility Location and Emergency Management (3 papers). The work is most often cited by research in Management Science and Operations Research (297 citations), Artificial Intelligence (485 citations), Computational Mathematics (8 citations), Computational Theory and Mathematics (120 citations) and Computer Networks and Communications (158 citations). Lev Reyzin has collaborated with scholars based in United States, Hungary and Romania. Frequent co-authors include Robert E. Schapire, Lihong Li, Wei Chu, Nikhil Srivastava, Elena Grigorescu, Santosh Vempala, Ying Xiao, Vitaly Feldman, Dana Angluin and James Aspnes. Their work appears in journals such as Theoretical Computer Science, Lecture notes in computer science, Journal of Combinatorial Optimization, Journal of the ACM 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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