Matthäus Kleindeßner
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- Ethics and Social Impacts of AI 3
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- Neural Networks and Applications 3
- Machine Learning and Algorithms 2
- Adversarial Robustness in Machine Learning 2
- Bayesian Methods and Mixture Models 1
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- Face and Expression Recognition 2
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- Advanced Statistical Methods and Models 2
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- Atmospheric and Environmental Gas Dynamics 1
- Co-authors
- Ulrike von LuxburgPranjal AwasthiJamie MorgensternAlex BeutelXuezhi WangChris RussellJianwu WangFrancesco Locatello
- Journals
- Journal of Machine Learning Research (1 paper)Frontiers in Big Data (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Matthäus Kleindeßner
11 papers receiving 114 citations
Peers
Comparison fields: 5 of 56
- Health Informatics 10
- Safety Research 30
- Artificial Intelligence 59
- Computer Vision and Pattern Recognition 27
- Computer Science Applications 5
Countries citing papers authored by Matthäus Kleindeßner
This map shows the geographic impact of Matthäus Kleindeßner'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 Matthäus Kleindeßner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthäus Kleindeßner more than expected).
Fields of papers citing papers by Matthäus Kleindeßner
This network shows the impact of papers produced by Matthäus Kleindeßner. 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 Matthäus Kleindeßner. The network helps show where Matthäus Kleindeßner may publish in the future.
Co-authorship network
The 20 scholars most cited alongside Matthäus Kleindeßner, 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 | 2024 | 4 | |
| 2 | 2022 | 7 | |
| 3 | 2022 | 14 | |
| 4 | Backward-Compatible Prediction Updates: A Probabilistic Approach | 2021 | 1 |
| 5 | 2021 | 22 | |
| 6 | 2021 | 14 | |
| 7 | Fair k-Center Clustering for Data Summarization | 2019 | 3 |
| 8 | 2019 | 18 | |
| 9 | Kernel functions based on triplet comparisons | 2017 | 5 |
| 10 | Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis | 2017 | 7 |
| 11 | Dimensionality estimation without distances | 2015 | 7 |
| 12 | Uniqueness of Ordinal Embedding | 2014 | 17 |
About Matthäus Kleindeßner
Matthäus Kleindeßner is a scholar working on Safety Research, Computer Graphics and Computer-Aided Design and Artificial Intelligence, having authored 12 papers that have together received 119 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Ethics and Social Impacts of AI (3 papers), Machine Learning and Algorithms (2 papers), Face and Expression Recognition (2 papers), Advanced Statistical Methods and Models (2 papers), Adversarial Robustness in Machine Learning (2 papers), Atmospheric and Environmental Gas Dynamics (1 paper) and Bayesian Methods and Mixture Models (1 paper). The work is most often cited by research in Health Informatics (10 citations), Safety Research (30 citations) and Artificial Intelligence (59 citations). Matthäus Kleindeßner has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Ulrike von Luxburg, Pranjal Awasthi, Jamie Morgenstern, Alex Beutel, Xuezhi Wang, Chris Russell, Jianwu Wang, Francesco Locatello, Bernhard Schölkopf and Pei Guo. Their work appears in journals such as Journal of Machine Learning Research, Frontiers in Big Data and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.