Matej Balog
Impact in
- Computational Mathematics top 5%
- Software top 10%
- Software Testing and Debugging Techniques
Papers in
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- Artificial Intelligence in Games 1
- Quantum Information and Cryptography 1
- Algorithms and Data Compression 1
- Evolutionary Algorithms and Applications 1
- Quantum Computing Algorithms and Architecture 1
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- Computability, Logic, AI Algorithms 1
- Co-authors
- Alexander Novikov (3 shared papers)Pushmeet Kohli (3 shared papers)Francisco J. R. Ruiz (3 shared papers)Alhussein Fawzi (3 shared papers)Mohammadamin Barekatain (3 shared papers)Bernardino Romera‐Paredes (3 shared papers)Daniel Tarlow (1 shared paper)Julian Schrittwieser (1 shared paper)
- Journals
- Nature (2 papers)Nature Machine Intelligence (1 paper)Apollo (University of Cambridge) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United KingdomGermanyNetherlands
In The Last Decade
Matej Balog
5 papers receiving 480 citations
Matej Balog's Hit Papers
Peers
Comparison fields: 5 of 91
- Computational Mathematics 19
- Software 47
- Artificial Intelligence 226
- Health Informatics 5
- Hardware and Architecture 26
Countries citing papers authored by Matej Balog
This map shows the geographic impact of Matej Balog'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 Matej Balog with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matej Balog more than expected).
Fields of papers citing papers by Matej Balog
This network shows the impact of papers produced by Matej Balog. 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 Matej Balog. The network helps show where Matej Balog may publish in the future.
Co-authors
The 25 scholars most cited alongside Matej Balog, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Discovering faster matrix multiplication algorithms with reinforcement learning Hit paper breakdown → | 2022 | 253 |
| 2 | Mathematical discoveries from program search with large language models Hit paper breakdown → | 2023 | 145 |
| 3 | 2016 | 96 | |
| 4 | 2017 | 5 | |
| 5 | 2025 | 4 |
About Matej Balog
Matej Balog is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Statistical and Nonlinear Physics and Computational Mathematics, having authored 5 papers that have together received 503 indexed citations. Recurring topics across this work include Artificial Intelligence in Games (1 paper), Statistical Mechanics and Entropy (1 paper), Quantum Information and Cryptography (1 paper), Algorithms and Data Compression (1 paper), Computability, Logic, AI Algorithms (1 paper), Tensor decomposition and applications (1 paper), Evolutionary Algorithms and Applications (1 paper) and Quantum Computing Algorithms and Architecture (1 paper). The work is most often cited by research in Computational Mathematics (19 citations), Software (47 citations), Artificial Intelligence (226 citations), Health Informatics (5 citations) and Hardware and Architecture (26 citations). Matej Balog has collaborated with scholars based in United Kingdom, Germany and Netherlands. Frequent co-authors include Alexander Novikov, Pushmeet Kohli, Francisco J. R. Ruiz, Alhussein Fawzi, Mohammadamin Barekatain, Bernardino Romera‐Paredes, Daniel Tarlow, Julian Schrittwieser, Sebastian Nowozin and Aja Huang. Their work appears in journals such as Nature, Nature Machine Intelligence, Apollo (University of Cambridge) 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.