Levente Kocsis
- Artificial Intelligence top 5%
- Information Systems top 10%
- Management Science and Operations Research top 10%
- Computer Networks and Communications top 10%
- Economics and Econometrics
- Co-authors
- Csaba SzepesváriAndrás GyörgyJan WillemsonAndrás A. BenczúrRóbert PálovicsDavid SilverSylvain GellyOlivier Teytaud
- Topics
- Recommender Systems and Techniques (9 papers)Advanced Bandit Algorithms Research (8 papers)Reinforcement Learning in Robotics (4 papers)
In The Last Decade
Levente Kocsis
24 papers receiving 350 citations
Peers
Comparison fields: 5 of 79
- Artificial Intelligence 218
- Information Systems 81
- Management Science and Operations Research 77
- Computer Networks and Communications 67
- Economics and Econometrics 53
Countries citing papers authored by Levente Kocsis
This map shows the geographic impact of Levente Kocsis'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 Levente Kocsis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Levente Kocsis more than expected).
Fields of papers citing papers by Levente Kocsis
This network shows the impact of papers produced by Levente Kocsis. 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 Levente Kocsis. The network helps show where Levente Kocsis may publish in the future.
Co-authorship network of co-authors of Levente Kocsis
This figure shows the co-authorship network connecting the top 25 collaborators of Levente Kocsis. A scholar is included among the top collaborators of Levente Kocsis 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 Levente Kocsis. Levente Kocsis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | Credit-to-GDP gap calculation using multivariate HP filter | 3 |
| 4 | Alpenglow: Open source recommender framework with time-Aware learning and evaluation | 3 |
| 5 | Online ranking prediction in non-stationary environments | 3 |
| 6 | 14 | |
| 7 | 18 | |
| 8 | Location-aware online learning for top-k hashtag recommendation | 4 |
| 9 | 3 | |
| 10 | 5 | |
| 11 | 1 | |
| 12 | 3 | |
| 13 | 82 | |
| 14 | Fraud Detection by Generating Positive Samples for Classification from Unlabeled Data | 2 |
| 15 | 41 | |
| 16 | Continuous time associative bandit problems | 14 |
| 17 | Improved Monte-Carlo Search | 63 |
| 18 | 14 | |
| 19 | 1 | |
| 20 | 1 |
About Levente Kocsis
Levente Kocsis is a scholar working on Management Science and Operations Research, Artificial Intelligence and Transportation, having authored 24 papers that have together received 373 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (9 papers), Advanced Bandit Algorithms Research (8 papers) and Reinforcement Learning in Robotics (4 papers). The work is most often cited by research in Artificial Intelligence (218 citations), Management Science and Operations Research (77 citations) and Computational Mathematics (3 citations). Levente Kocsis has collaborated with scholars based in Hungary, Canada and Japan. Frequent co-authors include Csaba Szepesvári, András György, Jan Willemson, András A. Benczúr, Róbert Pálovics, David Silver, Sylvain Gelly, Olivier Teytaud, Marc Schoenauer and Michèle Sébag. Their work appears in journals such as Communications of the ACM, Machine Learning and Perception.
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.