Luisa Zintgraf

1.5k total citations · 1 hit paper
14 papers, 322 citations indexed

About

Luisa Zintgraf is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Luisa Zintgraf has authored 14 papers receiving a total of 322 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Computational Theory and Mathematics. Recurrent topics in Luisa Zintgraf's work include Domain Adaptation and Few-Shot Learning (5 papers), Reinforcement Learning in Robotics (4 papers) and Multimodal Machine Learning Applications (3 papers). Luisa Zintgraf is often cited by papers focused on Domain Adaptation and Few-Shot Learning (5 papers), Reinforcement Learning in Robotics (4 papers) and Multimodal Machine Learning Applications (3 papers). Luisa Zintgraf collaborates with scholars based in United Kingdom, United States and Netherlands. Luisa Zintgraf's co-authors include Taco Cohen, Diederik M. Roijers, Max Welling, Tameem Adel, Ann Nowé, Gabriel de Oliveira Ramos, Eugenio Bargiacchi, Enda Howley, Matthew D Macfarlane and Richard Dazeley and has published in prestigious journals such as Nature, BMC Bioinformatics and Journal of Machine Learning Research.

In The Last Decade

Luisa Zintgraf

14 papers receiving 306 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A practical guide to multi-objective reinforcement learning and planning

2022 158 citations Conor F. Hayes, Roxana Rădulescu et al. Virtual Community of Pathological Anatomy (University of Castilla La Mancha) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Luisa Zintgraf United Kingdom	9	153	54	41	34	32	14	322
Avraham Ruderman Australia	4	246 1.6×	58 1.1×	31 0.8×	25 0.7×	21 0.7×	5	371
Bùi Ngọc Anh Vietnam	11	62 0.4×	68 1.3×	37 0.9×	14 0.4×	16 0.5×	34	286
Md Kamruzzaman Sarker United States	9	190 1.2×	102 1.9×	22 0.5×	17 0.5×	16 0.5×	32	397
Kazumi Nakamatsu Japan	10	150 1.0×	61 1.1×	72 1.8×	24 0.7×	13 0.4×	58	381
Haoran Tang China	6	270 1.8×	63 1.2×	47 1.1×	40 1.2×	19 0.6×	21	380
Baljeet Kaur India	11	204 1.3×	43 0.8×	61 1.5×	104 3.1×	54 1.7×	53	490
Jiayi Li China	11	179 1.2×	34 0.6×	51 1.2×	47 1.4×	8 0.3×	43	300
Harm van Seijen Canada	8	186 1.2×	34 0.6×	44 1.1×	49 1.4×	28 0.9×	17	294
Shun’ichi Tano Japan	9	112 0.7×	51 0.9×	45 1.1×	9 0.3×	16 0.5×	61	270
István Szita Hungary	8	416 2.7×	51 0.9×	50 1.2×	28 0.8×	20 0.6×	18	544

Countries citing papers authored by Luisa Zintgraf

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

14 of 14 papers shown

1.

Beck, Jacob, et al.. (2025). A Tutorial on Meta-Reinforcement Learning. 18(2-3). 224–384. 7 indexed citations

2.

Oh, Junhyuk, Dan A. Calian, Matteo Hessel, et al.. (2025). Discovering state-of-the-art reinforcement learning algorithms. Nature. 648(8093). 312–319. 1 indexed citations

3.

Hayes, Conor F., Roxana Rădulescu, Eugenio Bargiacchi, et al.. (2022). A practical guide to multi-objective reinforcement learning and planning. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 158 indexed citations breakdown →

4.

Zintgraf, Luisa, et al.. (2021). VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. Journal of Machine Learning Research. 22(289). 1–39. 9 indexed citations

5.

Zintgraf, Luisa, et al.. (2021). ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 10798–10808. 19 indexed citations

6.

Zintgraf, Luisa, et al.. (2021). Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors. City Research Online (City University London). 1–12. 28 indexed citations

7.

Zintgraf, Luisa, et al.. (2020). VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. arXiv (Cornell University). 9 indexed citations

8.

Roijers, Diederik M., Luisa Zintgraf, Pieter Libin, & Ann Nowé. (2020). Interactive multi-objective reinforcement learning in multi-armed bandits for any utility function. Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam). 6 indexed citations

9.

Zintgraf, Luisa, et al.. (2018). CAML: Fast Context Adaptation via Meta-Learning.. 8 indexed citations

10.

Vamplew, Peter, et al.. (2017). MORL-Glue: a benchmark suite for multi-objective reinforcement learning. Deakin Research Online (Deakin University). 5 indexed citations

11.

Zintgraf, Luisa, Taco Cohen, Tameem Adel, & Max Welling. (2017). Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 43 indexed citations

12.

Eck, A., Luisa Zintgraf, E. F. J. de Groot, et al.. (2017). Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinformatics. 18(1). 441–441. 20 indexed citations

13.

Zintgraf, Luisa, et al.. (2017). MultiMAuS: A Multi-Modal Authentication Simulator for Fraud Detection Research. VUBIR (Vrije Universiteit Brussel). 360–369. 1 indexed citations

14.

Zintgraf, Luisa, et al.. (2015). Quality Assessment of MORL Algorithms: A Utility-Based Approach. 8 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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