Sam Toyer
Impact in
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- AI-based Problem Solving and Planning
- Reinforcement Learning in Robotics
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
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- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
Papers in
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- Machine Learning and Algorithms 2
- AI-based Problem Solving and Planning 2
- Artificial Intelligence in Games 1
- Natural Language Processing Techniques 1
- Bayesian Modeling and Causal Inference 1
- Adversarial Robustness in Machine Learning 1
- Reinforcement Learning in Robotics 1
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- Video Surveillance and Tracking Methods 1
- Co-authors
- Felipe Trevizan (2 shared papers)Sylvie Thiébaux (2 shared papers)Lexing Xie (2 shared papers)Pieter Abbeel (2 shared papers)Sergey Levine (1 shared paper)Xue Bin Peng (1 shared paper)Tengda Han (1 shared paper)Stephen Jay Gould (1 shared paper)
- Journals
- Proceedings of the International Symposium on Combinatorial Search (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesAustralia
In The Last Decade
Sam Toyer
6 papers receiving 84 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 57
- Computer Vision and Pattern Recognition 36
- Software 3
- Health Informatics 1
- Control and Systems Engineering 14
Countries citing papers authored by Sam Toyer
This map shows the geographic impact of Sam Toyer'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 Sam Toyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Toyer more than expected).
Fields of papers citing papers by Sam Toyer
This network shows the impact of papers produced by Sam Toyer. 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 Sam Toyer. The network helps show where Sam Toyer may publish in the future.
Co-authors
The 24 scholars most cited alongside Sam Toyer, 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 | 2018 | 31 | |
| 2 | Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow | 2018 | 23 |
| 3 | 2017 | 20 | |
| 4 | 2025 | 6 | |
| 5 | 2021 | 5 | |
| 6 | The MAGICAL Benchmark for Robust Imitation | 2020 | 1 |
| 7 | 2024 | 0 |
About Sam Toyer
Sam Toyer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Media Technology and Infectious Diseases, having authored 7 papers that have together received 86 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (2 papers), AI-based Problem Solving and Planning (2 papers), Artificial Intelligence in Games (1 paper), Video Surveillance and Tracking Methods (1 paper), Natural Language Processing Techniques (1 paper), Bayesian Modeling and Causal Inference (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Artificial Intelligence (57 citations), Computer Vision and Pattern Recognition (36 citations), Software (3 citations), Health Informatics (1 citation) and Control and Systems Engineering (14 citations). Sam Toyer has collaborated with scholars based in United States and Australia. Frequent co-authors include Felipe Trevizan, Sylvie Thiébaux, Lexing Xie, Pieter Abbeel, Sergey Levine, Xue Bin Peng, Tengda Han, Stephen Jay Gould, Anoop Cherian and Angjoo Kanazawa. Their work appears in journals such as Proceedings of the International Symposium on Combinatorial Search, Proceedings of the AAAI Conference on Artificial Intelligence 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.