Luisa Zintgraf
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics top 10%
- Electrical and Electronic Engineering
- Cognitive Neuroscience
- Co-authors
- Diederik M. RoijersTaco CohenMax WellingTameem AdelAnn NowéKatja HofmannFredrik HeintzAthirai A. Irissappane
- Topics
- Domain Adaptation and Few-Shot Learning (5 papers)Reinforcement Learning in Robotics (4 papers)Multimodal Machine Learning Applications (3 papers)
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
Luisa Zintgraf
14 papers receiving 306 citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Artificial Intelligence 153
- Computer Vision and Pattern Recognition 54
- Computational Theory and Mathematics 41
- Electrical and Electronic Engineering 34
- Cognitive Neuroscience 32
Countries citing papers authored by Luisa Zintgraf
This map shows the geographic impact of Luisa Zintgraf'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 Luisa Zintgraf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luisa Zintgraf more than expected).
Fields of papers citing papers by Luisa Zintgraf
This network shows the impact of papers produced by Luisa Zintgraf. 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 Luisa Zintgraf. The network helps show where Luisa Zintgraf may publish in the future.
Co-authorship network of co-authors of Luisa Zintgraf
This figure shows the co-authorship network connecting the top 25 collaborators of Luisa Zintgraf. A scholar is included among the top collaborators of Luisa Zintgraf 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 Luisa Zintgraf. Luisa Zintgraf is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 1 | |
| 3 | A practical guide to multi-objective reinforcement learning and planningbreakdown → | 158 |
| 4 | VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning | 9 |
| 5 | 19 | |
| 6 | 28 | |
| 7 | VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning | 9 |
| 8 | Interactive multi-objective reinforcement learning in multi-armed bandits for any utility function | 6 |
| 9 | CAML: Fast Context Adaptation via Meta-Learning. | 8 |
| 10 | MORL-Glue: a benchmark suite for multi-objective reinforcement learning | 5 |
| 11 | Visualizing Deep Neural Network Decisions: Prediction Difference Analysis | 43 |
| 12 | 20 | |
| 13 | MultiMAuS: A Multi-Modal Authentication Simulator for Fraud Detection Research | 1 |
| 14 | Quality Assessment of MORL Algorithms: A Utility-Based Approach | 8 |
About Luisa Zintgraf
Luisa Zintgraf is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 322 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Reinforcement Learning in Robotics (4 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (153 citations) and Computer Vision and Pattern Recognition (54 citations). Luisa Zintgraf has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Diederik M. Roijers, Taco Cohen, Max Welling, Tameem Adel, Ann Nowé, Katja Hofmann, Fredrik Heintz, Athirai A. Irissappane, Patrick Mannion and Johan Källström. Their work appears in journals such as Nature, BMC Bioinformatics and Journal of Machine Learning Research.
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.