Edward Lockhart
Impact in
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- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Explainable Artificial Intelligence (XAI)
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Anomaly Detection Techniques and Applications
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- Multimodal Machine Learning Applications
Papers in
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- Reinforcement Learning in Robotics 3
- Adversarial Robustness in Machine Learning 2
- Artificial Intelligence in Games 1
- Explainable Artificial Intelligence (XAI) 1
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- EEG and Brain-Computer Interfaces 1
- Co-authors
- Timothy Lillicrap (1 shared paper)Victoria Langston (1 shared paper)David Reichert (1 shared paper)Adam Santoro (1 shared paper)Karl Tuyls (2 shared papers)Victor Bapst (1 shared paper)I. Babuschkin (1 shared paper)Peter Battaglia (1 shared paper)
- Journals
- International Conference on Machine Learning (1 paper)International Conference on Learning Representations (1 paper)Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United KingdomCanadaBrazil
In The Last Decade
Edward Lockhart
4 papers receiving 56 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 44
- Computer Vision and Pattern Recognition 9
- Transportation 2
- Communication 2
- Signal Processing 3
Countries citing papers authored by Edward Lockhart
This map shows the geographic impact of Edward Lockhart'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 Edward Lockhart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward Lockhart more than expected).
Fields of papers citing papers by Edward Lockhart
This network shows the impact of papers produced by Edward Lockhart. 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 Edward Lockhart. The network helps show where Edward Lockhart may publish in the future.
Co-authors
The 25 scholars most cited alongside Edward Lockhart, 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 | Deep reinforcement learning with relational inductive biases | 2018 | 48 |
| 2 | 2022 | 6 | |
| 3 | 2021 | 3 | |
| 4 | Fast computation of Nash Equilibria in Imperfect Information Games | 2020 | 1 |
About Edward Lockhart
Edward Lockhart is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Management Science and Operations Research, Infectious Diseases and Organic Chemistry, having authored 4 papers that have together received 58 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Adversarial Robustness in Machine Learning (2 papers), Artificial Intelligence in Games (1 paper), Advanced Bandit Algorithms Research (1 paper), EEG and Brain-Computer Interfaces (1 paper), Explainable Artificial Intelligence (XAI) (1 paper) and Game Theory and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (44 citations), Computer Vision and Pattern Recognition (9 citations), Transportation (2 citations), Communication (2 citations) and Signal Processing (3 citations). Edward Lockhart has collaborated with scholars based in United Kingdom, Canada and Brazil. Frequent co-authors include Timothy Lillicrap, Victoria Langston, David Reichert, Adam Santoro, Karl Tuyls, Victor Bapst, I. Babuschkin, Peter Battaglia, Vinícius Zambaldi and Matthew Botvinick. Their work appears in journals such as International Conference on Machine Learning, International Conference on Learning Representations, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence and Proceedings of the AAAI Conference on Artificial Intelligence.
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.