Matthew Riemer

1.1k total citations
13 papers, 187 citations indexed

About

Matthew Riemer is a scholar working on Artificial Intelligence, Management Science and Operations Research and Signal Processing. According to data from OpenAlex, Matthew Riemer has authored 13 papers receiving a total of 187 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 6 papers in Management Science and Operations Research and 2 papers in Signal Processing. Recurrent topics in Matthew Riemer's work include Reinforcement Learning in Robotics (8 papers), Topic Modeling (3 papers) and Advanced Bandit Algorithms Research (3 papers). Matthew Riemer is often cited by papers focused on Reinforcement Learning in Robotics (8 papers), Topic Modeling (3 papers) and Advanced Bandit Algorithms Research (3 papers). Matthew Riemer collaborates with scholars based in United States, Algeria and Mexico. Matthew Riemer's 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 and has published in prestigious journals such as Journal of Artificial Intelligence Research, arXiv (Cornell University) and International Conference on Machine Learning.

In The Last Decade

Matthew Riemer

13 papers receiving 177 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew Riemer United States 6 122 29 25 24 21 13 187
Steven Kapturowski United States 3 144 1.2× 30 1.0× 12 0.5× 34 1.4× 14 0.7× 5 194
André Barreto United States 8 176 1.4× 46 1.6× 19 0.8× 21 0.9× 14 0.7× 16 223
Akshay Agrawal United States 6 81 0.7× 21 0.7× 15 0.6× 29 1.2× 34 1.6× 16 199
Lisa Torrey United States 7 189 1.5× 67 2.3× 23 0.9× 24 1.0× 28 1.3× 15 264
Sanmit Narvekar United States 7 168 1.4× 46 1.6× 53 2.1× 32 1.3× 16 0.8× 11 232
Juan Carlos Santamaria United States 4 211 1.7× 57 2.0× 14 0.6× 35 1.5× 20 1.0× 8 260
Nicholay Topin United States 5 88 0.7× 16 0.6× 7 0.3× 24 1.0× 10 0.5× 9 143
Rahul Iyer United States 6 128 1.0× 16 0.6× 9 0.4× 17 0.7× 33 1.6× 13 188
Nikola Ivković Croatia 7 48 0.4× 15 0.5× 6 0.2× 21 0.9× 45 2.1× 38 145
Pierre Glize France 6 60 0.5× 9 0.3× 14 0.6× 9 0.4× 40 1.9× 22 104

Countries citing papers authored by Matthew Riemer

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

13 of 13 papers shown
1.
Riemer, Matthew, et al.. (2024). A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques. 5732–5745. 2 indexed citations
2.
Riemer, Matthew, et al.. (2022). Towards Continual Reinforcement Learning: A Review and Perspectives. Journal of Artificial Intelligence Research. 75. 1401–1476. 90 indexed citations
3.
Kim, Dong Ki, et al.. (2022). Context-Specific Representation Abstraction for Deep Option Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 36(6). 5959–5967. 3 indexed citations
4.
Kim, Dong Ki, Miao Liu, Matthew Riemer, et al.. (2021). A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. DSpace@MIT (Massachusetts Institute of Technology). 5541–5550. 1 indexed citations
5.
Sims, Chris R., et al.. (2021). Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games. 1–8. 1 indexed citations
6.
Klinger, Tim, et al.. (2021). RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence. 35(18). 15841–15842. 1 indexed citations
7.
Kim, Dong Ki, Miao Liu, Shayegan Omidshafiei, et al.. (2020). Learning Hierarchical Teaching Policies for Cooperative Agents. Adaptive Agents and Multi-Agents Systems. 620–628. 2 indexed citations
8.
Riemer, Matthew, et al.. (2020). Towards Continual Reinforcement Learning: A Review and Perspectives. arXiv (Cornell University). 11 indexed citations
9.
Cases, Ignacio, Matthew Riemer, Tim Klinger, et al.. (2019). Recursive Routing Networks: Learning to Compose Modules for Language Understanding. 3631–3648. 6 indexed citations
10.
Riemer, Matthew, Miao Liu, & Gerald Tesauro. (2018). Learning Abstract Options. arXiv (Cornell University). 31. 10424–10434. 12 indexed citations
11.
Omidshafiei, Shayegan, Dong Ki Kim, Miao Liu, et al.. (2018). Learning to Teach in Cooperative Multiagent Reinforcement Learning. DSpace@MIT (Massachusetts Institute of Technology). 49 indexed citations
12.
Riemer, Matthew, et al.. (2016). Correcting forecasts with multifactor neural attention. International Conference on Machine Learning. 3010–3019. 8 indexed citations
13.

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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