Ingmar Schuster
Impact in
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- Probabilistic and Robust Engineering Design
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- Model Reduction and Neural Networks
Papers in
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- Gaussian Processes and Bayesian Inference 4
- Natural Language Processing Techniques 1
- Bayesian Modeling and Causal Inference 1
- Neural Networks and Applications 1
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- Model Reduction and Neural Networks 3
- Co-authors
- Andrew Cheuk-Yin Ng (1 shared paper)Stefan Klus (3 shared papers)Krikamol Muandet (2 shared papers)T. J. Sullivan (1 shared paper)Christof Schütte (1 shared paper)Simone Prinz (1 shared paper)Luca Schulz (1 shared paper)Nicole Paczia (1 shared paper)
- Journals
- Journal of Nonlinear Science (1 paper)Knowledge-Based Systems (1 paper)ACS Synthetic Biology (1 paper)Journal of Machine Learning Research (1 paper)Journal of Computational and Graphical Statistics (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Ingmar Schuster
9 papers receiving 147 citations
Peers
Comparison fields: 5 of 83
- Statistics, Probability and Uncertainty 33
- Statistical and Nonlinear Physics 53
- Computational Mathematics 2
- Statistics and Probability 15
- Management Science and Operations Research 15
Countries citing papers authored by Ingmar Schuster
This map shows the geographic impact of Ingmar Schuster'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 Ingmar Schuster with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ingmar Schuster more than expected).
Fields of papers citing papers by Ingmar Schuster
This network shows the impact of papers produced by Ingmar Schuster. 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 Ingmar Schuster. The network helps show where Ingmar Schuster may publish in the future.
Co-authors
The 13 scholars most cited alongside Ingmar Schuster, 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 | Generalized Linear Models | 2012 | 62 |
| 2 | 2019 | 58 | |
| 3 | 2020 | 14 | |
| 4 | 2023 | 10 | |
| 5 | 2020 | 7 | |
| 6 | 2021 | 3 | |
| 7 | Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | 2021 | 2 |
| 8 | 2017 | 1 | |
| 9 | Probabilistic models of natural language semantics | 2015 | 1 |
About Ingmar Schuster
Ingmar Schuster is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability, Materials Chemistry and Molecular Biology, having authored 9 papers that have together received 158 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (4 papers), Model Reduction and Neural Networks (3 papers), Statistical Methods and Bayesian Inference (2 papers), Natural Language Processing Techniques (1 paper), Time Series Analysis and Forecasting (1 paper), Bayesian Modeling and Causal Inference (1 paper), Neural Networks and Applications (1 paper) and Markov Chains and Monte Carlo Methods (1 paper). The work is most often cited by research in Statistics, Probability and Uncertainty (33 citations), Statistical and Nonlinear Physics (53 citations), Computational Mathematics (2 citations), Statistics and Probability (15 citations) and Management Science and Operations Research (15 citations). Ingmar Schuster has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Andrew Cheuk-Yin Ng, Stefan Klus, Krikamol Muandet, T. J. Sullivan, Christof Schütte, Simone Prinz, Luca Schulz, Nicole Paczia, Jan Zarzycki and Tobias J. Erb. Their work appears in journals such as Journal of Nonlinear Science, Knowledge-Based Systems, ACS Synthetic Biology, Journal of Machine Learning Research and Journal of Computational and Graphical Statistics.
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.