Carlos Diuk
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Management Science and Operations Research top 10%
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Co-authors
- Michael L. LittmanYael NivMatthew BotvinickAndrew G. BartoAlec SolwayAlexander L. StrehlJoseph T. McGuireLihong Li
- Topics
- Reinforcement Learning in Robotics (7 papers)Machine Learning and Algorithms (6 papers)Neural dynamics and brain function (3 papers)
- Partner nations
- United StatesArgentinaCanada
In The Last Decade
Carlos Diuk
16 papers receiving 585 citations
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 348
- Cognitive Neuroscience 214
- Management Science and Operations Research 87
- Computer Vision and Pattern Recognition 47
- Control and Systems Engineering 44
Countries citing papers authored by Carlos Diuk
This map shows the geographic impact of Carlos Diuk'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 Carlos Diuk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carlos Diuk more than expected).
Fields of papers citing papers by Carlos Diuk
This network shows the impact of papers produced by Carlos Diuk. 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 Carlos Diuk. The network helps show where Carlos Diuk may publish in the future.
Co-authorship network of co-authors of Carlos Diuk
This figure shows the co-authorship network connecting the top 25 collaborators of Carlos Diuk. A scholar is included among the top collaborators of Carlos Diuk 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 Carlos Diuk. Carlos Diuk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 83 | |
| 3 | 2 | |
| 4 | 65 | |
| 5 | Compositional Policy Priors | 9 |
| 6 | 18 | |
| 7 | 141 | |
| 8 | Generalizing Apprenticeship Learning across Hypothesis Classes | 15 |
| 9 | The emergence of the modern concept of introspection: a quantitative linguistic analysis | 3 |
| 10 | 41 | |
| 11 | 2 | |
| 12 | 42 | |
| 13 | 127 | |
| 14 | Efficient structure learning in factored-state MDPs | 59 |
| 15 | 12 | |
| 16 | 1 |
About Carlos Diuk
Carlos Diuk is a scholar working on Artificial Intelligence, Cultural Studies and Management Science and Operations Research, having authored 16 papers that have together received 624 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Machine Learning and Algorithms (6 papers) and Neural dynamics and brain function (3 papers). The work is most often cited by research in General Decision Sciences (31 citations), Cognitive Neuroscience (214 citations) and Artificial Intelligence (348 citations). Carlos Diuk has collaborated with scholars based in United States, Argentina and Canada. Frequent co-authors include Michael L. Littman, Yael Niv, Matthew Botvinick, Andrew G. Barto, Alec Solway, Alexander L. Strehl, Joseph T. McGuire, Lihong Li, Thomas J. Walsh and Joni D. Wallis. Their work appears in journals such as Neuron, Journal of Neuroscience and PLoS Computational Biology.
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.