Valerio Perrone

39 total papers · 553 total citations
8 papers, 60 citations indexed

About

Valerio Perrone is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and General Social Sciences. According to data from OpenAlex, Valerio Perrone has authored 8 papers receiving a total of 60 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 1 paper in General Social Sciences. Recurrent topics in Valerio Perrone's work include Machine Learning and Data Classification (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers) and Machine Learning and Algorithms (2 papers). Valerio Perrone is often cited by papers focused on Machine Learning and Data Classification (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers) and Machine Learning and Algorithms (2 papers). Valerio Perrone collaborates with scholars based in United Kingdom, United States and Germany. Valerio Perrone's co-authors include Cédric Archambeau, Rodolphe Jenatton, Matthias Seeger, Yee Whye Teh, Paul A. Jenkins, Huibin Shen, Leonard Hasenclever, David Salinas, Michele Donini and Xiaoyu Sean Lu and has published in prestigious journals such as Journal of Machine Learning Research, Entropy and Oxford University Research Archive (ORA) (University of Oxford).

In The Last Decade

Valerio Perrone

8 papers receiving 57 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Valerio Perrone	48	15	9	7	6	8	60
Jan Pfeifer	44 0.9×	8 0.5×	11 1.2×	10 1.4×	7 1.2×	6	70
Shaohua Tan	48 1.0×	8 0.5×	12 1.3×	6 0.9×	6 1.0×	11	64
Nilesh Tripuraneni	72 1.5×	7 0.5×	6 0.7×	3 0.4×	9 1.5×	11	85
Ehsan Hajiramezanali	58 1.2×	9 0.6×	11 1.2×	3 0.4×	7 1.2×	8	95
Julien Ah-Pine	29 0.6×	5 0.3×	28 3.1×	4 0.6×	6 1.0×	10	68
Peter Bloem	47 1.0×	9 0.6×	10 1.1×	7 1.0×	12 2.0×	9	84
Dawid Ewald	47 1.0×	24 1.6×	7 0.8×		6 1.0×	8	69
Bangsheng Tang	32 0.7×	22 1.5×	17 1.9×	5 0.7×	9 1.5×	9	67
Daniel W. Stouch	21 0.4×	15 1.0×	21 2.3×	4 0.6×	3 0.5×	7	52
Andrew Critch	25 0.5×	16 1.1×	4 0.4×	3 0.4×	28 4.7×	11	63

Countries citing papers authored by Valerio Perrone

Since Specialization

Citations

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

Fields of papers citing papers by Valerio Perrone

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Valerio Perrone

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

All Works

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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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