Thore Graepel
Impact in
- Artificial Intelligence top 0.02%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
- Neural Networks and Applications
- Health Informatics top 0.5%
Papers in
-
- Machine Learning and Algorithms 22
- Reinforcement Learning in Robotics 13
- Artificial Intelligence in Games 12
- Neural Networks and Applications 11
- Machine Learning and Data Classification 9
-
- Advanced Bandit Algorithms Research 9
- Co-authors
- Demis HassabisDavid SilverArthur GuezLaurent SifreIoannis AntonoglouJulian SchrittwieserTimothy LillicrapGeorge van den Driessche
- Journals
- Nature (3 papers)Machine Learning (3 papers)ACM SIGPLAN Notices (2 papers)Science (2 papers)Journal of Machine Learning Research (2 papers)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Thore Graepel
102 papers receiving 19.6k citations
Hit Papers
Peers
Comparison fields: 5 of 213
- Artificial Intelligence 10.6k
- Health Informatics 239
- Computer Vision and Pattern Recognition 2.9k
- Management Science and Operations Research 1.4k
- Computational Theory and Mathematics 1.5k
Countries citing papers authored by Thore Graepel
This map shows the geographic impact of Thore Graepel'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 Thore Graepel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thore Graepel more than expected).
Fields of papers citing papers by Thore Graepel
This network shows the impact of papers produced by Thore Graepel. 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 Thore Graepel. The network helps show where Thore Graepel may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Thore Graepel, 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 | 2020 | 16 | |
| 2 | 2019 | 5 | |
| 3 | 2019 | 6 | |
| 4 | Human-level performance in 3D multiplayer games with population-based reinforcement learning Hit paper breakdown → | 2019 | 349 |
| 5 | 2018 | 229 | |
| 6 | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Hit paper breakdown → | 2018 | 1827 |
| 7 | The Mechanics of n-Player Differentiable Games | 2018 | 13 |
| 8 | Relational Forward Models for Multi-Agent Learning | 2018 | 7 |
| 9 | Re-evaluating evaluation | 2018 | 3 |
| 10 | 2014 | 17 | |
| 11 | Private traits and attributes are predictable from digital records of human behavior Hit paper breakdown → | 2013 | 1548 |
| 12 | Automated Probabilistic Modelling for Relational Data | 2013 | 1 |
| 13 | Comparing Feature-Based Models of Harmony | 2012 | 12 |
| 14 | Matchbox: Large Scale Bayesian Recommendations | 2009 | 6 |
| 15 | Structure from failure | 2007 | 16 |
| 16 | TrueSkill Through Time: Revisiting the History of Chess | 2007 | 58 |
| 17 | Generalization Bounds for the Area Under the ROC Curve | 2005 | 143 |
| 18 | Solving noisy linear operator equations by Gaussian processes: application to ordinary and partial differential equations | 2003 | 29 |
| 19 | From Margin to Sparsity | 2000 | 28 |
| 20 | A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work | 2000 | 33 |
About Thore Graepel
Thore Graepel is a scholar working on Artificial Intelligence, Management Science and Operations Research, Signal Processing, Safety Research and Statistical and Nonlinear Physics, having authored 103 papers that have together received 20.6k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (22 papers), Reinforcement Learning in Robotics (13 papers), Artificial Intelligence in Games (12 papers), Neural Networks and Applications (11 papers), Experimental Behavioral Economics Studies (10 papers), Face and Expression Recognition (9 papers), Machine Learning and Data Classification (9 papers) and Advanced Bandit Algorithms Research (9 papers). The work is most often cited by research in Artificial Intelligence (10.6k citations), Health Informatics (239 citations), Computer Vision and Pattern Recognition (2.9k citations), Management Science and Operations Research (1.4k citations) and Computational Theory and Mathematics (1.5k citations). Thore Graepel has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Demis Hassabis, David Silver, Arthur Guez, Laurent Sifre, Ioannis Antonoglou, Julian Schrittwieser, Timothy Lillicrap, George van den Driessche, Aja Huang and Michał Kosiński. Their work appears in journals such as Nature, Machine Learning, ACM SIGPLAN Notices, Science and Journal of Machine Learning Research.
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.