Mark van der Wilk
- Artificial Intelligence top 10%
- Automotive Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality top 10%
- Co-authors
- Yarin GalAlex KendallRoberto CipollaRowan McAllisterAdrian WellerAnoop ShahCarl Edward RasmussenAmar Shah
- Topics
- Gaussian Processes and Bayesian Inference (8 papers)Machine Learning and Data Classification (6 papers)Advanced Multi-Objective Optimization Algorithms (5 papers)
- Journals
- Biotechnology and BioengineeringComputers & Chemical EngineeringSoftware Impacts
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Mark van der Wilk
17 papers receiving 310 citations
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 171
- Automotive Engineering 102
- Computer Vision and Pattern Recognition 81
- Control and Systems Engineering 51
- Safety, Risk, Reliability and Quality 35
Countries citing papers authored by Mark van der Wilk
This map shows the geographic impact of Mark van der Wilk'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 Mark van der Wilk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark van der Wilk more than expected).
Fields of papers citing papers by Mark van der Wilk
This network shows the impact of papers produced by Mark van der Wilk. 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 Mark van der Wilk. The network helps show where Mark van der Wilk may publish in the future.
Co-authorship network of co-authors of Mark van der Wilk
This figure shows the co-authorship network connecting the top 25 collaborators of Mark van der Wilk. A scholar is included among the top collaborators of Mark van der Wilk 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 Mark van der Wilk. Mark van der Wilk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 23 | |
| 6 | Speedy Performance Estimation for Neural Architecture Search | 2 |
| 7 | 4 | |
| 8 | A Bayesian Perspective on Training Speed and Model Selection | 3 |
| 9 | Understanding Variational Inference in Function-Space | 1 |
| 10 | Bayesian Layers: A Module for Neural Network Uncertainty | 12 |
| 11 | 4 | |
| 12 | Variational Gaussian Process Models without Matrix Inverses. | 0 |
| 13 | 12 | |
| 14 | Learning Invariances using the Marginal Likelihood | 6 |
| 15 | 141 | |
| 16 | 66 | |
| 17 | 14 | |
| 18 | 26 |
About Mark van der Wilk
Mark van der Wilk is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Automotive Engineering, having authored 18 papers that have together received 321 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (8 papers), Machine Learning and Data Classification (6 papers) and Advanced Multi-Objective Optimization Algorithms (5 papers). The work is most often cited by research in Automotive Engineering (102 citations), Artificial Intelligence (171 citations) and Computer Vision and Pattern Recognition (81 citations). Mark van der Wilk has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Yarin Gal, Alex Kendall, Roberto Cipolla, Rowan McAllister, Adrian Weller, Anoop Shah, Carl Edward Rasmussen, Amar Shah, James Hensman and David R. Burt. Their work appears in journals such as Biotechnology and Bioengineering, Computers & Chemical Engineering and Software Impacts.
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.