Carlos Diuk

1.1k total citations
16 papers, 624 citations indexed

About

Carlos Diuk is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Cultural Studies. According to data from OpenAlex, Carlos Diuk has authored 16 papers receiving a total of 624 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 3 papers in Cognitive Neuroscience and 2 papers in Cultural Studies. Recurrent topics in Carlos Diuk's work include Reinforcement Learning in Robotics (7 papers), Machine Learning and Algorithms (6 papers) and Neural dynamics and brain function (3 papers). Carlos Diuk is often cited by papers focused on Reinforcement Learning in Robotics (7 papers), Machine Learning and Algorithms (6 papers) and Neural dynamics and brain function (3 papers). Carlos Diuk collaborates with scholars based in United States, Argentina and Canada. Carlos Diuk's 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 and has published in prestigious journals such as Neuron, Journal of Neuroscience and PLoS Computational Biology.

In The Last Decade

Carlos Diuk

16 papers receiving 585 citations

Peers

Countries citing papers authored by Carlos Diuk

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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16 of 16 papers shown

#	Work	Indexed citations
1	Social Catalysts: Characterizing People Who Spark Conversations Among Others 2021 ·Proceedings of the ACM on Human-Computer Interaction ·Martin Saveski, Farshad Kooti, (unknown), Carlos Diuk, (unknown), Lada A. Adamic	4
2	Optimal Behavioral Hierarchy 2014 ·PLoS Computational Biology ·Alec Solway, Carlos Diuk, Natalia I. Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew Botvinick	83
3	Parsing Heuristic and Forward Search in First‐Graders' Game‐Play Behavior 2014 ·Cognitive Science ·(unknown), Andrea Paula Goldin, Carlos Diuk, Mariano Sigman	2
4	Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia 2013 ·Journal of Neuroscience ·Carlos Diuk, (unknown), Joni D. Wallis, Matthew Botvinick, Yael Niv	65
5	Compositional Policy Priors 2013 ·DSpace@MIT (Massachusetts Institute of Technology) ·David Wingate, Carlos Diuk, Timothy J. O’Donnell, (unknown), Samuel J. Gershman	9
6	A quantitative philology of introspection 2012 ·Frontiers in Integrative Neuroscience ·Carlos Diuk, Diego Fernández Slezak, (unknown), Mariano Sigman, Guillermo Cecchi	18
7	A Neural Signature of Hierarchical Reinforcement Learning 2011 ·Neuron ·(unknown), Alec Solway, Carlos Diuk, Joseph T. McGuire, Andrew G. Barto, Yael Niv, Matthew Botvinick	141
8	Generalizing Apprenticeship Learning across Hypothesis Classes 2010 ·International Conference on Machine Learning ·Thomas J. Walsh, K.A. Subramanian, Michael L. Littman, Carlos Diuk	15
9	The emergence of the modern concept of introspection: a quantitative linguistic analysis 2010 ·North American Chapter of the Association for Computational Linguistics ·(unknown), Diego Fernández Slezak, Carlos Diuk, Guillermo Cecchi	3
10	The adaptive k -meteorologists problem and its application to structure learning and feature selection in reinforcement learning 2009 ·Carlos Diuk, Lihong Li, (unknown)	41
11	Workshop summary: Results of the 2009 reinforcement learning competition 2009 ·David Wingate, Carlos Diuk, Lihong Li, Matthew E. Taylor, (unknown)	2
12	Exploring compact reinforcement-learning representations with linear regression 2009 ·View ·Thomas J. Walsh, István Szita, Michael L. Littman, Carlos Diuk	42
13	An object-oriented representation for efficient reinforcement learning 2008 ·Rutgers University Community Repository (Rutgers University) ·Carlos Diuk, (unknown), Michael L. Littman	127
14	Efficient structure learning in factored-state MDPs 2007 ·National Conference on Artificial Intelligence ·Alexander L. Strehl, Carlos Diuk, Michael L. Littman	59
15	A hierarchical approach to efficient reinforcement learning in deterministic domains 2006 ·Carlos Diuk, Alexander L. Strehl, Michael L. Littman	12
16	COMPUTER TOOLS FOR GENEALOGICAL RECONSTRUCTION 2000 ·Carlos Diuk, Ènrique Tandeter	1

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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