Matthew Riemer
- Artificial Intelligence top 10%
- Control and Systems Engineering
- Management Science and Operations Research
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Co-authors
- Doina PrecupIrina RishGerald TesauroDong Ki KimJonathan P. HowShayegan OmidshafieiMiao LiuChristopher Amato
- Topics
- Reinforcement Learning in Robotics (8 papers)Topic Modeling (3 papers)Advanced Bandit Algorithms Research (3 papers)
- Cited by
- Artificial IntelligenceManagement Science and Operations ResearchComputational Theory and Mathematics
- Journals
- Journal of Artificial Intelligence ResearcharXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- United StatesAlgeriaMexico
In The Last Decade
Matthew Riemer
13 papers receiving 177 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 122
- Control and Systems Engineering 29
- Management Science and Operations Research 25
- Computer Vision and Pattern Recognition 24
- Computer Networks and Communications 21
Countries citing papers authored by Matthew Riemer
This map shows the geographic impact of Matthew Riemer'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 Matthew Riemer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Riemer more than expected).
Fields of papers citing papers by Matthew Riemer
This network shows the impact of papers produced by Matthew Riemer. 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 Matthew Riemer. The network helps show where Matthew Riemer may publish in the future.
Co-authorship network of co-authors of Matthew Riemer
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Riemer. A scholar is included among the top collaborators of Matthew Riemer 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 Matthew Riemer. Matthew Riemer 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 | 90 | |
| 3 | 3 | |
| 4 | A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning | 1 |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 11 | |
| 9 | 6 | |
| 10 | Learning Abstract Options | 12 |
| 11 | Learning to Teach in Cooperative Multiagent Reinforcement Learning | 49 |
| 12 | Correcting forecasts with multifactor neural attention | 8 |
| 13 | 1 |
About Matthew Riemer
Matthew Riemer is a scholar working on Management Science and Operations Research, Artificial Intelligence and Signal Processing, having authored 13 papers that have together received 187 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Topic Modeling (3 papers) and Advanced Bandit Algorithms Research (3 papers). The work is most often cited by research in Artificial Intelligence (122 citations), Management Science and Operations Research (25 citations) and Computational Theory and Mathematics (20 citations). Matthew Riemer has collaborated with scholars based in United States, Algeria and Mexico. Frequent co-authors include Doina Precup, Irina Rish, Gerald Tesauro, Dong Ki Kim, Jonathan P. How, Shayegan Omidshafiei, Miao Liu, Christopher Amato, Murray Campbell and Miao Liu. Their work appears in journals such as Journal of Artificial Intelligence Research, arXiv (Cornell University) and International Conference on Machine Learning.
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.