Andrea Tacchetti

2.0k total citations
21 papers, 322 citations indexed

About

Andrea Tacchetti is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Management Science and Operations Research. According to data from OpenAlex, Andrea Tacchetti has authored 21 papers receiving a total of 322 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Cognitive Neuroscience and 6 papers in Management Science and Operations Research. Recurrent topics in Andrea Tacchetti's work include Neural dynamics and brain function (5 papers), Experimental Behavioral Economics Studies (5 papers) and Auction Theory and Applications (5 papers). Andrea Tacchetti is often cited by papers focused on Neural dynamics and brain function (5 papers), Experimental Behavioral Economics Studies (5 papers) and Auction Theory and Applications (5 papers). Andrea Tacchetti collaborates with scholars based in United States, United Kingdom and Netherlands. Andrea Tacchetti's co-authors include Tomaso Poggio, Leyla Işık, Lorenzo Rosasco, Peter Battaglia, Théophane Weber, Daniel Zoran, Jim Mutch, Joel Z. Leibo, Razvan Pascanu and Nicholas Watters and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Neurophysiology.

In The Last Decade

Andrea Tacchetti

21 papers receiving 303 citations

Peers

Andrea Tacchetti

CVPR

Antonio García Castañeda United Kingdom

Christoph Salge United Kingdom

Jordi Grau-Moya Germany

James MacGlashan United States

Dylan Hadfield-Menell United States

Sarath Chandar Canada

Robert Marinier United States

Stefan Janaqi France

Nigel Crook United Kingdom

Antonio García Castañeda United Kingdom

Andrea Tacchetti

241 ×2.1

102 ×1.1

CVPR

23 ×0.3

17 ×0.5

34 ×1.1

Citations per year, relative to Andrea Tacchetti Andrea Tacchetti (= 1×) peers Antonio García Castañeda

Countries citing papers authored by Andrea Tacchetti

Since Specialization

Citations

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

Fields of papers citing papers by Andrea Tacchetti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrea Tacchetti

This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Tacchetti. A scholar is included among the top collaborators of Andrea Tacchetti 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 Andrea Tacchetti. Andrea Tacchetti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Koster, Raphaël, Andrea Tacchetti, Jan Balaguer, et al.. (2025). Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem. Nature Communications. 16(1). 2824–2824. 2 indexed citations

Tacchetti, Andrea, Raphaël Koster, Jan Balaguer, et al.. (2025). Deep mechanism design: Learning social and economic policies for human benefit. Proceedings of the National Academy of Sciences. 122(25). e2319949121–e2319949121. 2 indexed citations

McKee, Kevin R., Andrea Tacchetti, Michiel A. Bakker, et al.. (2023). Scaffolding cooperation in human groups with deep reinforcement learning. Nature Human Behaviour. 7(10). 1787–1796. 12 indexed citations

Koster, Raphaël, Jan Balaguer, Andrea Tacchetti, et al.. (2022). Human-centred mechanism design with Democratic AI. Nature Human Behaviour. 6(10). 1398–1407. 51 indexed citations

Anthony, T, et al.. (2022). Designing all-pay auctions using deep learning and multi-agent simulation. Scientific Reports. 12(1). 16937–16937. 1 indexed citations

Kramár, János, Tom Eccles, Andrea Tacchetti, et al.. (2022). Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy. Nature Communications. 13(1). 7214–7214. 11 indexed citations

Fu, Justin, Andrea Tacchetti, Julien Pérolat, & Yoram Bachrach. (2021). Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning. Journal of Artificial Intelligence Research. 71. 925–951. 7 indexed citations

Tacchetti, Andrea, Hui Song, Pedro A. M. Mediano, et al.. (2018). Relational Forward Models for Multi-Agent Learning. UCL Discovery (University College London). 7 indexed citations

Tacchetti, Andrea, et al.. (2018). Trading robust representations for sample complexity through self-supervised visual experience. Neural Information Processing Systems. 31. 9617–9627. 1 indexed citations

10.

Tacchetti, Andrea, Leyla Işık, & Tomaso Poggio. (2018). Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science. 4(1). 403–422. 23 indexed citations

11.

Watters, Nicholas, Daniel Zoran, Théophane Weber, et al.. (2017). Visual Interaction Networks: Learning a Physics Simulator from Video. Neural Information Processing Systems. 30. 4539–4547. 74 indexed citations

12.

Tacchetti, Andrea, Leyla Işık, & Tomaso Poggio. (2017). Invariant recognition drives neural representations of action sequences. PLoS Computational Biology. 13(12). e1005859–e1005859. 11 indexed citations

13.

Tacchetti, Andrea, Leyla Işık, & Tomaso Poggio. (2016). Spatio-temporal convolutional neural networks explain human neural representations of action recognition. arXiv (Cornell University). 4 indexed citations

14.

Anselmi, Fabio, Joel Z. Leibo, Lorenzo Rosasco, et al.. (2015). Unsupervised learning of invariant representations. Theoretical Computer Science. 633. 112–121. 42 indexed citations

15.

Tacchetti, Andrea, Leyla Işık, & Tomaso Poggio. (2015). Invariant representations for action recognition in the visual system. Journal of Vision. 15(12). 558–558. 1 indexed citations

16.

Anselmi, Fabio, Joel Z. Leibo, Lorenzo Rosasco, et al.. (2014). Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. 14 indexed citations

17.

Rosasco, Lorenzo, Andrea Tacchetti, & Silvia Villa. (2014). Regularization by Early Stopping for Online Learning Algorithms.. 9 indexed citations

18.

Poggio, Tomaso, Jim Mutch, Joel Z. Leibo, Lorenzo Rosasco, & Andrea Tacchetti. (2012). The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).. DSpace@MIT (Massachusetts Institute of Technology). 13 indexed citations

19.

Butcher, Mark, A. Masi, Michele Martino, & Andrea Tacchetti. (2012). Implementation and tuning of the Extended Kalman Filter for a sensorless drive working with arbitrary stepper motors and cable lengths. CERN Bulletin. 98. 2216–2222. 5 indexed citations

20.

Tacchetti, Andrea, et al.. (2012). GURLS: a Toolbox for Regularized Least Squares Learning. DSpace@MIT (Massachusetts Institute of Technology). 7 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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