Jan Leike

7.3k total citations
11 papers, 44 citations indexed

About

Jan Leike is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Jan Leike has authored 11 papers receiving a total of 44 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 3 papers in Management Science and Operations Research. Recurrent topics in Jan Leike's work include Machine Learning and Algorithms (4 papers), Reinforcement Learning in Robotics (4 papers) and Evolutionary Algorithms and Applications (3 papers). Jan Leike is often cited by papers focused on Machine Learning and Algorithms (4 papers), Reinforcement Learning in Robotics (4 papers) and Evolutionary Algorithms and Applications (3 papers). Jan Leike collaborates with scholars based in Australia, Canada and United Kingdom. Jan Leike's co-authors include Matthias Heizmann, Marcus Hütter, Edward Hughes, Dzmitry Bahdanau, Edward Grefenstette, Felix Hill, Pushmeet Kohli, Daniel Dietsch, Tor Lattimore and Andreas Podelski and has published in prestigious journals such as Theoretical Computer Science, Logical Methods in Computer Science and ANU Open Research (Australian National University).

In The Last Decade

Jan Leike

9 papers receiving 42 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jan Leike Australia	4	36	15	14	8	4	11	44
Vitaly Kurin United Kingdom	6	36 1.0×	7 0.5×	22 1.6×	3 0.4×	4 1.0×	7	59
Valerio Perrone United Kingdom	5	48 1.3×	15 1.0×	9 0.6×	2 0.3×	3 0.8×	8	60
Sumith Kulal United States	4	21 0.6×	9 0.6×	18 1.3×	4 0.5×	5 1.3×	7	43
Nitika Verma India	3	10 0.3×	25 1.7×	8 0.6×	13 1.6×	3 0.8×	3	38
Daniel Selsam United States	3	40 1.1×	6 0.4×	8 0.6×	4 0.5×	3 0.8×	3	65
Daniel Kühlwein Germany	3	62 1.7×	28 1.9×	4 0.3×	10 1.3×	5 1.3×	10	70
Michał Dereziński United States	4	24 0.7×	8 0.5×	10 0.7×		3 0.8×	18	43
Benjamin Monmege France	5	32 0.9×	40 2.7×	3 0.2×	13 1.6×	2 0.5×	12	55
Andrew Cropper United Kingdom	5	74 2.1×	11 0.7×	6 0.4×	2 0.3×	6 1.5×	19	84

Countries citing papers authored by Jan Leike

Since Specialization

Citations

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

Fields of papers citing papers by Jan Leike

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jan Leike

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

All Works

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11 of 11 papers shown

1.

Bahdanau, Dzmitry, Felix Hill, Jan Leike, et al.. (2018). Learning to Follow Language Instructions with Adversarial Reward Induction. arXiv (Cornell University). 4 indexed citations

2.

Bahdanau, Dzmitry, Felix Hill, Jan Leike, et al.. (2018). Jointly Learning "What" and "How" from Instructions and Goal-States.. International Conference on Learning Representations. 2 indexed citations

3.

Bahdanau, Dzmitry, Felix Hill, Jan Leike, et al.. (2018). Learning to Understand Goal Specifications by Modelling Reward. arXiv (Cornell University). 16 indexed citations

4.

Aslanides, John, et al.. (2017). Generalised Discount Functions applied to a Monte-Carlo AI u Implementation. Adaptive Agents and Multi-Agents Systems. 1589–1591.

5.

Leike, Jan, Tor Lattimore, Laurent Orseau, & Marcus Hütter. (2017). On Thompson Sampling and Asymptotic Optimality. ANU Open Research (Australian National University). 4889–4893. 3 indexed citations

6.

Leike, Jan & Marcus Hütter. (2017). On the computability of Solomonoff induction and AIXI. Theoretical Computer Science. 716. 28–49. 3 indexed citations

7.

Leike, Jan, Tor Lattimore, Laurent Orseau, & Marcus Hütter. (2016). Thompson sampling is asymptotically optimal in general environments. ANU Open Research (Australian National University). 417–426.

8.

Heizmann, Matthias, et al.. (2015). Ultimate Automizer with Array Interpolation - (Competition Contribution).. ANU Open Research (Australian National University). 455–457. 3 indexed citations

9.

Leike, Jan & Marcus Hütter. (2015). Bad Universal Priors and Notions of Optimality. ANU Open Research (Australian National University). 1244–1259. 4 indexed citations

10.

Leike, Jan & Matthias Heizmann. (2015). Ranking Templates for Linear Loops. Logical Methods in Computer Science. Volume 11, Issue 1. 8 indexed citations

11.

Leike, Jan & Marcus Hütter. (2015). On the Computability of AIXI. arXiv (Cornell University). 1 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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