André Barreto

950 total citations
16 papers, 223 citations indexed

About

André Barreto is a scholar working on Artificial Intelligence, Management Information Systems and Information Systems. According to data from OpenAlex, André Barreto has authored 16 papers receiving a total of 223 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 2 papers in Management Information Systems and 2 papers in Information Systems. Recurrent topics in André Barreto's work include Reinforcement Learning in Robotics (14 papers), Evolutionary Algorithms and Applications (6 papers) and Supply Chain and Inventory Management (2 papers). André Barreto is often cited by papers focused on Reinforcement Learning in Robotics (14 papers), Evolutionary Algorithms and Applications (6 papers) and Supply Chain and Inventory Management (2 papers). André Barreto collaborates with scholars based in United States, Canada and United Kingdom. André Barreto's co-authors include David Silver, Charles W. Anderson, Tom Schaul, Doina Precup, Diana Borsa, Jonathan J. Hunt, Rémi Munos, Shaobo Hou, Will Dabney and Hado P. van Hasselt and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Artificial Intelligence and BMC Genomics.

In The Last Decade

André Barreto

16 papers receiving 216 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
André Barreto United States	8	176	46	44	25	21	16	223
Steven Kapturowski United States	3	144 0.8×	30 0.7×	21 0.5×	18 0.7×	34 1.6×	5	194
Harm van Seijen Canada	8	186 1.1×	45 1.0×	44 1.0×	28 1.1×	34 1.6×	17	294
Matthew Riemer United States	6	122 0.7×	29 0.6×	20 0.5×	10 0.4×	24 1.1×	13	187
Matthieu Geist France	10	176 1.0×	52 1.1×	20 0.5×	8 0.3×	29 1.4×	27	261
Juan Carlos Santamaria United States	4	211 1.2×	57 1.2×	32 0.7×	13 0.5×	35 1.7×	8	260
Matteo Pirotta Italy	11	174 1.0×	48 1.0×	99 2.3×	10 0.4×	16 0.8×	28	265
Bei Peng United States	8	159 0.9×	61 1.3×	10 0.2×	12 0.5×	21 1.0×	18	199
Chenjia Bai China	6	94 0.5×	41 0.9×	19 0.4×	10 0.4×	27 1.3×	26	173
Povilas Daniušis Lithuania	5	163 0.9×	16 0.3×	32 0.7×	64 2.6×	30 1.4×	11	277
Lisa Torrey United States	7	189 1.1×	67 1.5×	19 0.4×	5 0.2×	24 1.1×	15	264

Countries citing papers authored by André Barreto

Since Specialization

Citations

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

Fields of papers citing papers by André Barreto

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of André Barreto

This figure shows the co-authorship network connecting the top 25 collaborators of André Barreto. A scholar is included among the top collaborators of André Barreto 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 André Barreto. André Barreto 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

Sprechmann, Pablo, Steven Hansen, André Barreto, et al.. (2021). Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning. arXiv (Cornell University). 1 indexed citations

Wen, Zheng, et al.. (2020). On Efficiency in Hierarchical Reinforcement Learning. Neural Information Processing Systems. 33. 6708–6718. 9 indexed citations

Barreto, André, Shaobo Hou, Diana Borsa, David Silver, & Doina Precup. (2020). Fast reinforcement learning with generalized policy updates. Proceedings of the National Academy of Sciences. 117(48). 30079–30087. 37 indexed citations

Riquelme, Carlos, Hugo Penedones, Damien Vincent, et al.. (2019). Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. arXiv (Cornell University). 32. 11872–11882. 3 indexed citations

Barreto, André, Diana Borsa, Shaobo Hou, et al.. (2019). The Option Keyboard: Combining Skills in Reinforcement Learning. arXiv (Cornell University). 32. 13031–13041. 15 indexed citations

Barreto, André, Will Dabney, Rémi Munos, et al.. (2019). Successor Features for Transfer in Reinforcement Learning. UCL Discovery (University College London). 30. 4055–4065. 50 indexed citations

Barreto, André, Diana Borsa, John Quan, et al.. (2019). Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. arXiv (Cornell University). 501–510. 19 indexed citations

Precup, Doina, et al.. (2018). Knowledge Representation for Reinforcement Learning using General Value Functions. 1 indexed citations

Mendonça, Matheus R. F., Artur Ziviani, & André Barreto. (2018). Abstract State Transition Graphs for Model-Based Reinforcement Learning. 8. 115–120. 1 indexed citations

10.

Hunt, Jonathan J., André Barreto, Timothy Lillicrap, & Nicolas Heess. (2018). Composing Entropic Policies using Divergence Correction. arXiv (Cornell University). 2911–2920. 4 indexed citations

11.

Hansen, Steven, Pablo Sprechmann, Alexander Pritzel, André Barreto, & Charles Blundell. (2018). Fast deep reinforcement learning using online adjustments from the past. arXiv (Cornell University). 31. 10567–10577. 7 indexed citations

12.

Silver, David, Hado van Hasselt, Matteo Hessel, et al.. (2017). The predictron: end-to-end learning and planning. International Conference on Machine Learning. 3191–3199. 25 indexed citations

13.

Farahmand, Amir massoud, André Barreto, & Daniel Nikovski. (2017). Value-Aware Loss Function for Model-based Reinforcement Learning. PolyPublie (École Polytechnique de Montréal). 1486–1494. 7 indexed citations

14.

Barreto, André, et al.. (2016). Incremental Stochastic Factorization for Online Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 5 indexed citations

15.

Barreto, André, et al.. (2012). Analysis of composition-based metagenomic classification. BMC Genomics. 13(Suppl 5). S1–S1. 4 indexed citations

16.

Barreto, André & Charles W. Anderson. (2007). Restricted gradient-descent algorithm for value-function approximation in reinforcement learning. Artificial Intelligence. 172(4-5). 454–482. 35 indexed citations

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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