Julia Herbinger
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Machine Learning and Data Classification
- Machine Learning in Healthcare
- Adversarial Robustness in Machine Learning
Papers in
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- Machine Learning and Data Classification 5
- Explainable Artificial Intelligence (XAI) 3
- Data Stream Mining Techniques 2
- Bayesian Modeling and Causal Inference 1
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- Monetary Policy and Economic Impact 2
- Co-authors
- Giuseppe Casalicchio (6 shared papers)Bernd Bischl (5 shared papers)Gunnar König (2 shared papers)Christoph Molnar (2 shared papers)Timo Freiesleben (2 shared papers)Moritz Grosse‐Wentrup (1 shared paper)Quay Au (1 shared paper)Clemens Stachl (1 shared paper)
- Journals
- Remote Sensing (1 paper)Annals of Operations Research (1 paper)Data Mining and Knowledge Discovery (1 paper)Lecture notes in computer science (1 paper)Communications in computer and information science (2 papers)
In The Last Decade
Julia Herbinger
9 papers receiving 222 citations
Peers
Comparison fields: 5 of 96
- Health Informatics 13
- Artificial Intelligence 106
- Health Information Management 12
- Environmental Engineering 25
- Information Systems and Management 11
Countries citing papers authored by Julia Herbinger
This map shows the geographic impact of Julia Herbinger'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 Julia Herbinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julia Herbinger more than expected).
Fields of papers citing papers by Julia Herbinger
This network shows the impact of papers produced by Julia Herbinger. 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 Julia Herbinger. The network helps show where Julia Herbinger may publish in the future.
Co-authors
The 16 scholars most cited alongside Julia Herbinger, 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 | 2022 | 99 | |
| 2 | 2023 | 59 | |
| 3 | 2022 | 43 | |
| 4 | 2022 | 13 | |
| 5 | 2021 | 9 | |
| 6 | 2021 | 6 | |
| 7 | 2024 | 1 | |
| 8 | Towards Explaining Hyperparameter Optimization via Partial Dependence Plots | 2021 | 1 |
| 9 | 2019 | 1 |
About Julia Herbinger
Julia Herbinger is a scholar working on Artificial Intelligence, General Economics, Econometrics and Finance, Economics and Econometrics, Statistics and Probability and Finance, having authored 9 papers that have together received 232 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (5 papers), Explainable Artificial Intelligence (XAI) (3 papers), Data Stream Mining Techniques (2 papers), Statistical Methods and Inference (2 papers), Monetary Policy and Economic Impact (2 papers), Market Dynamics and Volatility (2 papers), Soil Moisture and Remote Sensing (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (106 citations), Health Information Management (12 citations), Environmental Engineering (25 citations) and Information Systems and Management (11 citations). Julia Herbinger has collaborated with scholars based in Germany, Austria and Spain. Frequent co-authors include Giuseppe Casalicchio, Bernd Bischl, Gunnar König, Christoph Molnar, Timo Freiesleben, Moritz Grosse‐Wentrup, Quay Au, Clemens Stachl, Marvin N. Wright and Frank Thomas Seifried. Their work appears in journals such as Remote Sensing, Annals of Operations Research, Data Mining and Knowledge Discovery, Lecture notes in computer science and Communications in computer and information science.
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.