Ingmar Schuster

481 total citations
9 papers, 158 citations indexed

About

Ingmar Schuster is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Statistics and Probability. According to data from OpenAlex, Ingmar Schuster has authored 9 papers receiving a total of 158 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Statistical and Nonlinear Physics and 2 papers in Statistics and Probability. Recurrent topics in Ingmar Schuster's work include Gaussian Processes and Bayesian Inference (4 papers), Model Reduction and Neural Networks (3 papers) and Statistical Methods and Bayesian Inference (2 papers). Ingmar Schuster is often cited by papers focused on Gaussian Processes and Bayesian Inference (4 papers), Model Reduction and Neural Networks (3 papers) and Statistical Methods and Bayesian Inference (2 papers). Ingmar Schuster collaborates with scholars based in Germany, United Kingdom and United States. Ingmar Schuster's co-authors include Andrew Cheuk-Yin Ng, Krikamol Muandet, Stefan Klus, T. J. Sullivan, Jan Zarzycki, Simone Prinz, Christof Schütte, Luca Schulz, Tobias J. Erb and Nicole Paczia and has published in prestigious journals such as Journal of Machine Learning Research, Knowledge-Based Systems and Journal of Computational and Graphical Statistics.

In The Last Decade

Ingmar Schuster

9 papers receiving 147 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ingmar Schuster Germany	5	53	35	33	19	15	9	158
Aimé Lachal France	10	45 0.8×	18 0.5×	9 0.3×	21 1.1×	48 3.2×	34	274
Namgil Lee South Korea	9	21 0.4×	35 1.0×	7 0.2×	42 2.2×	11 0.7×	23	226
Vigirdas Mackevičius Lithuania	8	29 0.5×	11 0.3×	12 0.4×	7 0.4×	7 0.5×	32	202
Claudia Totzeck Germany	6	68 1.3×	59 1.7×	4 0.1×	11 0.6×	31 2.1×	19	224
Laurent Carraro France	9	11 0.2×	32 0.9×	71 2.2×	5 0.3×	7 0.5×	15	185
Gustavo L. Gilardoni Brazil	9	39 0.7×	22 0.6×	70 2.1×	8 0.4×	4 0.3×	18	256
Davide Pigoli United Kingdom	9	13 0.2×	96 2.7×	15 0.5×	6 0.3×	19 1.3×	19	245
Motonobu Kanagawa Japan	6	17 0.3×	72 2.1×	21 0.6×	5 0.3×	5 0.3×	12	131
Cencheng Shen United States	8	36 0.7×	57 1.6×	12 0.4×	4 0.2×	34 2.3×	25	188
Jerzy Ombach Poland	9	66 1.2×	24 0.7×	7 0.2×	3 0.2×	5 0.3×	19	205

Countries citing papers authored by Ingmar Schuster

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Ingmar Schuster

This figure shows the co-authorship network connecting the top 25 collaborators of Ingmar Schuster. A scholar is included among the top collaborators of Ingmar Schuster 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 Ingmar Schuster. Ingmar Schuster is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Schulz, Luca, Ingmar Schuster, Nicole Paczia, et al.. (2023). Machine Learning-Supported Enzyme Engineering toward Improved CO₂-Fixation of Glycolyl-CoA Carboxylase. ACS Synthetic Biology. 12(12). 3521–3530. 10 indexed citations

2.

Rasul, Kashif, et al.. (2021). Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. arXiv (Cornell University). 2 indexed citations

3.

Klus, Stefan, et al.. (2021). Feature space approximation for kernel-based supervised learning. Knowledge-Based Systems. 221. 106935–106935. 3 indexed citations

4.

Schuster, Ingmar, et al.. (2020). Markov Chain Importance Sampling—A Highly Efficient Estimator for MCMC. Journal of Computational and Graphical Statistics. 30(2). 260–268. 7 indexed citations

5.

Schuster, Ingmar, et al.. (2020). A Rigorous Theory of Conditional Mean Embeddings. SIAM Journal on Mathematics of Data Science. 2(3). 583–606. 14 indexed citations

6.

Klus, Stefan, Ingmar Schuster, & Krikamol Muandet. (2019). Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. Journal of Nonlinear Science. 30(1). 283–315. 58 indexed citations

7.

Klus, Stefan, Ingmar Schuster, & Krikamol Muandet. (2017). Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. Journal of Machine Learning Research. 1 indexed citations

8.

Schuster, Ingmar. (2015). Probabilistic models of natural language semantics. Qucosa (Saxon State and University Library Dresden). 1 indexed citations

9.

Schuster, Ingmar, et al.. (2012). Generalized Linear Models. 62 indexed citations

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.

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