Matej Balog

1.0k total citations · 2 hit papers
5 papers, 470 citations indexed

About

Matej Balog is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, Matej Balog has authored 5 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computational Theory and Mathematics and 1 paper in Information Systems. Recurrent topics in Matej Balog's work include Gaussian Processes and Bayesian Inference (1 paper), Quantum Computing Algorithms and Architecture (1 paper) and Computability, Logic, AI Algorithms (1 paper). Matej Balog is often cited by papers focused on Gaussian Processes and Bayesian Inference (1 paper), Quantum Computing Algorithms and Architecture (1 paper) and Computability, Logic, AI Algorithms (1 paper). Matej Balog collaborates with scholars based in United Kingdom, Germany and Netherlands. Matej Balog's co-authors include Alexander Novikov, Mohammadamin Barekatain, Francisco J. R. Ruiz, Bernardino Romera‐Paredes, Alhussein Fawzi, Pushmeet Kohli, Sebastian Nowozin, David Silver, Thomas Hubert and Aja Huang and has published in prestigious journals such as Nature, Nature Machine Intelligence and Apollo (University of Cambridge).

In The Last Decade

Matej Balog

5 papers receiving 438 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Discovering faster matrix multiplication algorithms with reinforcement learning

2022 244 citations Alhussein Fawzi, Matej Balog et al. Nature profile →
Mathematical discoveries from program search with large language models

2023 124 citations Bernardino Romera‐Paredes, Mohammadamin Barekatain et al. Nature profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Matej Balog United Kingdom	4	218	86	52	47	47	5	470
Rafael C. Carrasco Spain	15	467 2.1×	89 1.0×	138 2.7×	37 0.8×	31 0.7×	36	683
Gilad Katz Israel	14	324 1.5×	145 1.7×	21 0.4×	34 0.7×	155 3.3×	55	650
T. Raita Finland	9	409 1.9×	154 1.8×	67 1.3×	40 0.9×	31 0.7×	29	733
Xuhong Zhang China	13	235 1.1×	132 1.5×	8 0.2×	43 0.9×	32 0.7×	85	502
Alexander G. Ororbia United States	12	314 1.4×	94 1.1×	17 0.3×	9 0.2×	41 0.9×	43	569
Qipeng Guo China	11	907 4.2×	118 1.4×	26 0.5×	17 0.4×	46 1.0×	22	1.1k
Christopher Rouff United States	15	442 2.0×	100 1.2×	142 2.7×	125 2.7×	51 1.1×	83	833
George K. Thiruvathukal United States	13	150 0.7×	125 1.5×	19 0.4×	32 0.7×	54 1.1×	109	582
Virendrakumar C. Bhavsar Canada	11	224 1.0×	84 1.0×	60 1.2×	5 0.1×	50 1.1×	51	467

Countries citing papers authored by Matej Balog

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Matej Balog

This figure shows the co-authorship network connecting the top 25 collaborators of Matej Balog. A scholar is included among the top collaborators of Matej Balog 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 Matej Balog. Matej Balog is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

5 of 5 papers shown

1.

Ruiz, Francisco J. R., Johannes Bausch, Matej Balog, et al.. (2025). Quantum circuit optimization with AlphaTensor. Nature Machine Intelligence. 7(3). 374–385. 2 indexed citations

2.

Romera‐Paredes, Bernardino, Mohammadamin Barekatain, Alexander Novikov, et al.. (2023). Mathematical discoveries from program search with large language models. Nature. 625(7995). 468–475. 124 indexed citations breakdown →

3.

Fawzi, Alhussein, Matej Balog, Aja Huang, et al.. (2022). Discovering faster matrix multiplication algorithms with reinforcement learning. Nature. 610(7930). 47–53. 244 indexed citations breakdown →

4.

Balog, Matej, Nilesh Tripuraneni, Zoubin Ghahramani, & Adrian Weller. (2017). Lost Relatives of the Gumbel Trick. Apollo (University of Cambridge). 371–379. 4 indexed citations

5.

Balog, Matej, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, & Daniel Tarlow. (2016). DeepCoder: Learning to Write Programs. arXiv (Cornell University). 96 indexed citations

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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