Valerio Perrone
Impact in
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- Machine Learning and Data Classification
- Gaussian Processes and Bayesian Inference
- Machine Learning and Algorithms
- Metaheuristic Optimization Algorithms Research
- Domain Adaptation and Few-Shot Learning
- Bayesian Methods and Mixture Models
Papers in
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- Machine Learning and Data Classification 6
- Machine Learning and Algorithms 2
- Domain Adaptation and Few-Shot Learning 1
- Gaussian Processes and Bayesian Inference 1
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- Advanced Multi-Objective Optimization Algorithms 4
- Co-authors
- Cédric Archambeau (4 shared papers)Matthias Seeger (3 shared papers)Rodolphe Jenatton (2 shared papers)Yee Whye Teh (2 shared papers)Paul A. Jenkins (1 shared paper)Huibin Shen (2 shared papers)Michele Donini (2 shared papers)Manfred Opper (1 shared paper)
- Journals
- Journal of Machine Learning Research (1 paper)Entropy (1 paper)Florence Research (University of Florence) (1 paper)Oxford University Research Archive (ORA) (University of Oxford) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Valerio Perrone
8 papers receiving 62 citations
Peers
Comparison fields: 5 of 33
- Computational Mathematics 2
- Artificial Intelligence 50
- Computational Theory and Mathematics 15
- Statistics and Probability 7
- Signal Processing 7
Countries citing papers authored by Valerio Perrone
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
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-authors
The 16 scholars most cited alongside Valerio Perrone, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Scalable Hyperparameter Transfer Learning | 2018 | 25 |
| 2 | 2016 | 10 | |
| 3 | 2019 | 8 | |
| 4 | Relativistic Monte Carlo | 2017 | 5 |
| 5 | 2021 | 5 | |
| 6 | Multi-objective multi-fidelity hyperparameter optimization with application to fairness | 2020 | 5 |
| 7 | 2019 | 3 | |
| 8 | 2025 | 3 |
About Valerio Perrone
Valerio Perrone is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, General Social Sciences, Statistics and Probability and Materials Chemistry, having authored 8 papers that have together received 64 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers), Machine Learning and Algorithms (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Computational and Text Analysis Methods (1 paper), Markov Chains and Monte Carlo Methods (1 paper), Gaussian Processes and Bayesian Inference (1 paper) and Machine Learning in Materials Science (1 paper). The work is most often cited by research in Computational Mathematics (2 citations), Artificial Intelligence (50 citations), Computational Theory and Mathematics (15 citations), Statistics and Probability (7 citations) and Signal Processing (7 citations). Valerio Perrone has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Cédric Archambeau, Matthias Seeger, Rodolphe Jenatton, Yee Whye Teh, Paul A. Jenkins, Huibin Shen, Michele Donini, Manfred Opper, Xiaoyu Sean Lu and David Salinas. Their work appears in journals such as Journal of Machine Learning Research, Entropy, Florence Research (University of Florence), Oxford University Research Archive (ORA) (University of Oxford) and Neural Information Processing Systems.
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.