Jan Leike

7.3k citations
11 papers · 44 · h-index 4

Impact in

    • Software Testing and Debugging Techniques
    • Reinforcement Learning in Robotics
    • Topic Modeling
    • Logic, programming, and type systems
    • Natural Language Processing Techniques
    • Machine Learning and Algorithms

Papers in

Jan Leike

9 papers receiving 42 citations

Peers

Jan Leike
Comparison fields: 5 of 19
  • Software 8
  • Artificial Intelligence 36
  • Computational Theory and Mathematics 15
  • Computer Vision and Pattern Recognition 14
  • General Decision Sciences 1
Replace Vitaly Kurin with:
Vitaly Kurin United Kingdom
Daniel Selsam United States
Sumith Kulal United States
Daniel Kühlwein Germany
Valerio Perrone United Kingdom
Nitika Verma India
Liana Hadarean United States
Andrew Cropper United Kingdom
David Nowak Japan
Benjamin Monmege France
Jan Leike relative to Vitaly Kurin United Kingdom Vitaly Kurin's profile →
Citations per field
00.5×2.7×
Vitaly Kurin · 1×
Citations per year

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-authors

The 12 scholars most cited alongside Jan Leike, 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 Jan Leike Line = papers co-authored together Jan Leike links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 201816
2 20158
3
Bad Universal Priors and Notions of Optimality
20154
4
Learning to Follow Language Instructions with Adversarial Reward Induction
20184
5 20153
6 20173
7 20173
8
Jointly Learning "What" and "How" from Instructions and Goal-States.
20182
9 20151
10 20170
11
Thompson sampling is asymptotically optimal in general environments
20160

About Jan Leike

Jan Leike is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 11 papers that have together received 44 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (4 papers), Reinforcement Learning in Robotics (4 papers), Evolutionary Algorithms and Applications (3 papers), Computability, Logic, AI Algorithms (3 papers), Multimodal Machine Learning Applications (2 papers), Advanced Bandit Algorithms Research (2 papers), Natural Language Processing Techniques (1 paper) and Logic, programming, and type systems (1 paper). The work is most often cited by research in Software (8 citations), Artificial Intelligence (36 citations), Computational Theory and Mathematics (15 citations), Computer Vision and Pattern Recognition (14 citations) and General Decision Sciences (1 citation). Jan Leike has collaborated with scholars based in Australia, Canada and United Kingdom. Frequent co-authors include Marcus Hütter, Matthias Heizmann, Dzmitry Bahdanau, Edward Grefenstette, Edward Hughes, Felix Hill, Pushmeet Kohli, Andreas Podelski, Tor Lattimore and Laurent Orseau. Their work appears in journals such as Logical Methods in Computer Science, Theoretical Computer Science, ANU Open Research (Australian National University), International Conference on Learning Representations 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.

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