Michele Donini

935 total citations
36 papers, 417 citations indexed

About

Michele Donini is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Safety Research. According to data from OpenAlex, Michele Donini has authored 36 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 6 papers in Safety Research. Recurrent topics in Michele Donini's work include Machine Learning and Data Classification (11 papers), Face and Expression Recognition (9 papers) and Machine Learning and Algorithms (6 papers). Michele Donini is often cited by papers focused on Machine Learning and Data Classification (11 papers), Face and Expression Recognition (9 papers) and Machine Learning and Algorithms (6 papers). Michele Donini collaborates with scholars based in Italy, United Kingdom and United States. Michele Donini's co-authors include Fabio Aiolli, Massimiliano Pontil, Luca Oneto, Nicolò Navarin, Ombretta Gaggi, Matteo Ciman, John Shawe‐Taylor, Alessandro Sperduti, Davide Anguita and Krishnaram Kenthapadi and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and BMC Bioinformatics.

In The Last Decade

Michele Donini

36 papers receiving 412 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michele Donini Italy 13 230 117 46 43 26 36 417
Nicolò Navarin Italy 15 325 1.4× 105 0.9× 71 1.5× 35 0.8× 17 0.7× 73 581
Sarath Chandar Canada 9 374 1.6× 124 1.1× 19 0.4× 16 0.4× 28 1.1× 26 511
Josua Krause United States 5 335 1.5× 241 2.1× 19 0.4× 34 0.8× 58 2.2× 7 494
Khuyagbaatar Batsuren Italy 9 237 1.0× 59 0.5× 39 0.8× 28 0.7× 12 0.5× 12 351
Gábor Bella Italy 11 279 1.2× 69 0.6× 23 0.5× 27 0.6× 14 0.5× 38 431
James Atwood United States 4 217 0.9× 103 0.9× 15 0.3× 51 1.2× 6 0.2× 7 360
Yao Ming China 10 311 1.4× 237 2.0× 19 0.4× 26 0.6× 39 1.5× 30 530
Zonghan Yang China 4 307 1.3× 100 0.9× 34 0.7× 7 0.2× 22 0.8× 11 540
Yusheng Su China 6 271 1.2× 82 0.7× 28 0.6× 7 0.2× 23 0.9× 10 522
Chang Su China 11 126 0.5× 74 0.6× 34 0.7× 20 0.5× 55 2.1× 38 329

Countries citing papers authored by Michele Donini

Since Specialization
Citations

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

Fields of papers citing papers by Michele Donini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michele Donini

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

All Works

20 of 20 papers shown
1.
Donini, Michele, et al.. (2023). Geographical Erasure in Language Generation. 12310–12324. 1 indexed citations
2.
Das, Sanjiv Ranjan, Michele Donini, Muhammad Bilal Zafar, John Cijiang He, & Krishnaram Kenthapadi. (2021). FinLex: An effective use of word embeddings for financial lexicon generation. SHILAP Revista de lepidopterología. 8. 1–11. 5 indexed citations
3.
Donini, Michele, et al.. (2021). Voting with random classifiers (VORACE): theoretical and experimental analysis. Autonomous Agents and Multi-Agent Systems. 35(2). 15 indexed citations
4.
Oneto, Luca, Michele Donini, Massimiliano Pontil, & John Shawe‐Taylor. (2020). Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing. 416. 231–243. 8 indexed citations
5.
Oneto, Luca, Nicolò Navarin, & Michele Donini. (2020). Learning Deep Fair Graph Neural Networks.. The European Symposium on Artificial Neural Networks. 31–36. 5 indexed citations
6.
Oneto, Luca, Michele Donini, & Massimiliano Pontil. (2020). General Fair Empirical Risk Minimization. CINECA IRIS Institutial Research Information System (University of Genoa). 14 indexed citations
7.
Oneto, Luca, et al.. (2020). Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. Neural Information Processing Systems. 33. 15360–15370. 12 indexed citations
8.
Oneto, Luca, Michele Donini, Massimiliano Pontil, & Andreas Maurer. (2020). Learning Fair and Transferable Representations with Theoretical Guarantees. CINECA IRIS Institutial Research Information System (University of Genoa). 30–39. 7 indexed citations
9.
Donini, Michele, João M. Monteiro, Massimiliano Pontil, et al.. (2019). Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage. 195. 215–231. 15 indexed citations
10.
Oneto, Luca, Nicolò Navarin, Michele Donini, & Davide Anguita. (2018). Emerging trends in machine learning: beyond conventional methods and data.. Research Padua Archive (University of Padua). 1 indexed citations
11.
Zampieri, Guido, Tran Van Dinh, Michele Donini, et al.. (2018). Scuba: scalable kernel-based gene prioritization. BMC Bioinformatics. 19(1). 23–23. 14 indexed citations
12.
Donini, Michele, Andrea Loreggia, Maria Pini, & Francesca Rossi. (2018). Voting with random neural networks: A democratic ensemble classifier. Institutional Research Information System (Università degli Studi di Brescia). 2 indexed citations
13.
Oneto, Luca, Nicolò Navarin, Michele Donini, et al.. (2017). Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing. 268. 4–16. 9 indexed citations
14.
Franceschi, Luca, Michele Donini, Paolo Frasconi, & Massimiliano Pontil. (2017). On Hyperparameter Optimization in Learning Systems.. International Conference on Learning Representations. 4 indexed citations
15.
Oneto, Luca, Nicolò Navarin, Michele Donini, et al.. (2017). Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. IEEE Transactions on Neural Networks and Learning Systems. 29(10). 4660–4671. 8 indexed citations
16.
Lauriola, Ivano, Michele Donini, & Fabio Aiolli. (2017). Learning dot-product polynomials for multiclass problems.. The European Symposium on Artificial Neural Networks. 2 indexed citations
17.
Donini, Michele, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, & Fabrizio Costa. (2017). Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning.. Research Padua Archive (University of Padua). 4 indexed citations
18.
Oneto, Luca, Nicolò Navarin, Michele Donini, et al.. (2016). Measuring the Expressivity of Graph Kernels through the Rademacher Complexity.. CINECA IRIS Institutial Research Information System (University of Genoa). 23–28. 1 indexed citations
19.
Bolón‐Canedo, Verónica, Michele Donini, & Fabio Aiolli. (2015). Feature and kernel learning.. Research Padua Archive (University of Padua). 7 indexed citations
20.
Aiolli, Fabio & Michele Donini. (2014). Easy multiple kernel learning. Research Padua Archive (University of Padua). 4 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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