Maximilian Igl
- Artificial Intelligence
- Automotive Engineering
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Signal Processing
- Co-authors
- Shimon WhitesonKatja HofmannMark PalatucciGregory FarquharPunit ShahAlex KueflerDragomir AnguelovBrandyn White
- Topics
- Reinforcement Learning in Robotics (8 papers)Adversarial Robustness in Machine Learning (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)
- Journals
- Journal of Machine Learning ResearchOxford University Research Archive (ORA) (University of Oxford)arXiv (Cornell University)
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
Maximilian Igl
11 papers receiving 80 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 47
- Automotive Engineering 25
- Computer Vision and Pattern Recognition 21
- Control and Systems Engineering 19
- Signal Processing 8
Countries citing papers authored by Maximilian Igl
This map shows the geographic impact of Maximilian Igl'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 Maximilian Igl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximilian Igl more than expected).
Fields of papers citing papers by Maximilian Igl
This network shows the impact of papers produced by Maximilian Igl. 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 Maximilian Igl. The network helps show where Maximilian Igl may publish in the future.
Co-authorship network of co-authors of Maximilian Igl
This figure shows the co-authorship network connecting the top 25 collaborators of Maximilian Igl. A scholar is included among the top collaborators of Maximilian Igl 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 Maximilian Igl. Maximilian Igl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 26 | |
| 5 | VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning | 9 |
| 6 | VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning | 9 |
| 7 | 5 | |
| 8 | Generalization in Reinforcement Learning with Selective Noise Injection and Information | 1 |
| 9 | 14 | |
| 10 | Auto-Encoding Sequential Monte Carlo | 10 |
| 11 | TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning | 4 |
| 12 | 5 |
About Maximilian Igl
Maximilian Igl is a scholar working on Artificial Intelligence, Automotive Engineering and Computational Theory and Mathematics, having authored 12 papers that have together received 85 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Adversarial Robustness in Machine Learning (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Automotive Engineering (25 citations), Artificial Intelligence (47 citations) and Computer Vision and Pattern Recognition (21 citations). Maximilian Igl has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Shimon Whiteson, Katja Hofmann, Mark Palatucci, Gregory Farquhar, Punit Shah, Alex Kuefler, Dragomir Anguelov, Brandyn White, Dae Woo Kim and Yarin Gal. Their work appears in journals such as Journal of Machine Learning Research, Oxford University Research Archive (ORA) (University of Oxford) 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.