Michael Pearce

519 citations
16 papers · 239 · h-index 8

Impact in

Papers in

Michael Pearce

16 papers receiving 229 citations

Peers

Michael Pearce
Comparison fields: 5 of 68
  • Management Science and Operations Research 55
  • Computational Theory and Mathematics 63
  • Artificial Intelligence 108
  • Statistics, Probability and Uncertainty 19
  • Computer Vision and Pattern Recognition 55
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Sarah Dean United States
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Citations per year

Countries citing papers authored by Michael Pearce

Since Specialization
Citations

This map shows the geographic impact of Michael Pearce'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 Michael Pearce with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Pearce more than expected).

Fields of papers citing papers by Michael Pearce

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Pearce. 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 Michael Pearce. The network helps show where Michael Pearce may publish in the future.

Co-authors

The 18 scholars most cited alongside Michael Pearce, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Pearce Line = papers co-authored together Michael Pearce links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 199475
2 201964
3 201826
4 201814
5 201811
6 202210
7 20178
8 20178
9 20176
10 20195
11
The Gaussian Process Prior VAE for Interpretable Latent Dynamics from Pixels
20194
12 20052
13 20202
14
Efficient information collection on portfolios
20172
15 20171
16 20181

About Michael Pearce

Michael Pearce is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computational Theory and Mathematics, Computer Vision and Pattern Recognition and Marketing, having authored 16 papers that have together received 239 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (7 papers), Gaussian Processes and Bayesian Inference (6 papers), Simulation Techniques and Applications (4 papers), Advanced Bandit Algorithms Research (4 papers), AI-based Problem Solving and Planning (2 papers), Robotic Path Planning Algorithms (2 papers), Auction Theory and Applications (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Management Science and Operations Research (55 citations), Computational Theory and Mathematics (63 citations), Artificial Intelligence (108 citations), Statistics, Probability and Uncertainty (19 citations) and Computer Vision and Pattern Recognition (55 citations). Michael Pearce has collaborated with scholars based in United Kingdom, United States and Sweden. Frequent co-authors include Juergen Branke, Ronald C. Arkin, Gary Boone, Ashwin Ram, Matthias Poloczek, David Eriksson, Jacob R. Gardner, R.D. Turner, Simon Day and Siew Wan Hee. Their work appears in journals such as Pharmaceutical Statistics, BMC Medical Research Methodology, European Journal of Operational Research, Adaptive Behavior and ACM Transactions on Modeling and Computer Simulation.

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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