Michele Donini
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Safety Research top 10%
- Signal Processing
- Co-authors
- Fabio AiolliMassimiliano PontilLuca OnetoNicolò NavarinOmbretta GaggiMatteo CimanJohn Shawe‐TaylorAlessandro Sperduti
- Topics
- Machine Learning and Data Classification (11 papers)Face and Expression Recognition (9 papers)Machine Learning and Algorithms (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaNeuroImageBMC Bioinformatics
- Partner nations
- ItalyUnited KingdomUnited States
In The Last Decade
Michele Donini
36 papers receiving 412 citations
Peers
Comparison fields: 5 of 93
- Artificial Intelligence 230
- Computer Vision and Pattern Recognition 117
- Molecular Biology 46
- Safety Research 43
- Signal Processing 26
Countries citing papers authored by Michele Donini
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 15 | |
| 4 | 8 | |
| 5 | Learning Deep Fair Graph Neural Networks. | 5 |
| 6 | 14 | |
| 7 | Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning | 12 |
| 8 | 7 | |
| 9 | 15 | |
| 10 | Emerging trends in machine learning: beyond conventional methods and data. | 1 |
| 11 | 14 | |
| 12 | Voting with random neural networks: A democratic ensemble classifier | 2 |
| 13 | 9 | |
| 14 | On Hyperparameter Optimization in Learning Systems. | 4 |
| 15 | 8 | |
| 16 | Learning dot-product polynomials for multiclass problems. | 2 |
| 17 | Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. | 4 |
| 18 | Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. | 1 |
| 19 | Feature and kernel learning. | 7 |
| 20 | Easy multiple kernel learning | 4 |
About Michele Donini
Michele Donini is a scholar working on Computational Mathematics, Artificial Intelligence and Safety Research, having authored 36 papers that have together received 417 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (11 papers), Face and Expression Recognition (9 papers) and Machine Learning and Algorithms (6 papers). The work is most often cited by research in Artificial Intelligence (230 citations), Computer Vision and Pattern Recognition (117 citations) and Safety Research (43 citations). Michele Donini has collaborated with scholars based in Italy, United Kingdom and United States. Frequent 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. Their work appears in journals such as SHILAP Revista de lepidopterología, NeuroImage and BMC Bioinformatics.
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.