Research output per year
Research output per year
Personal profile
Research Interests
Professor Filippone's current research interests include the development of tractable and scalable Bayesian inference techniques for Gaussian processes and Deep Learning models, with applications in life and environmental sciences.
Education
Statistics, Postdoc, University of Glasgow
2011 → 2012
Computer Science, Postdoc, University of Sheffield
2009 → 2010
Computer Science, PhD, University of Genoa
2008
Visiting Scholar, George Mason University
2007
Physics, MSc, University of Genoa
2004
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel
Wacker, J., Ohana, R. & Filippone, M., 2023, p. 5181-5212. 32 p.Research output: Contribution to conference › Paper › peer-review
1 Scopus citations -
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Tran, B. H., Shahbaba, B., Mandt, S. & Filippone, M., 2023, p. 34409-34430. 22 p.Research output: Contribution to conference › Paper › peer-review
2 Scopus citations -
How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models
Franzese, G., Rossi, S., Yang, L., Finamore, A., Rossi, D., Filippone, M. & Michiardi, P., Apr 2023, In: Entropy. 25, 4, 633.Research output: Contribution to journal › Article › peer-review
Open Access7 Scopus citations -
Imposing Functional Priors on Bayesian Neural Networks
Kozyrskiy, B., Milios, D. & Filippone, M., 2023, ICPRAM 2023 - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, Volume 1. De Marsico, M., Sanniti di Baja, G. & Fred, A. L. N. (eds.). Science and Technology Publications, Lda, p. 450-457 8 p. (International Conference on Pattern Recognition Applications and Methods; vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access -
All You Need is a Good Functional Prior for Bayesian Deep Learning
Tran, B. H., Rossi, S., Milios, D. & Filippone, M., 2022, In: Journal of Machine Learning Research. 23Research output: Contribution to journal › Article › peer-review
21 Scopus citations -
Assessing Bayesian Semi-Parametric Log-Linear Models: An Application to Disclosure Risk Estimation
Carota, C., Filippone, M. & Polettini, S., Apr 2022, In: International Statistical Review. 90, 1, p. 165-183 19 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Deep Compositional Spatial Models
Zammit-Mangion, A., Ng, T. L. J., Vu, Q. & Filippone, M., 2022, In: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. 117, 540, p. 1787-1808 22 p.Research output: Contribution to journal › Article › peer-review
Open Access25 Scopus citations -
Locally Smoothed Gaussian Process Regression
Gogolashvili, D., Kozyrskiy, B. & Filippone, M., 2022, p. 2717-2726. 10 p.Research output: Contribution to conference › Paper › peer-review
Open Access6 Scopus citations
Press/Media
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Strengthening nonparametric Bayesian methods with structured kernels
11/9/22
1 item of Media coverage
Press/Media: Press / Media
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LightOn Unveils LightOn Appliance, Photonic Co-processor
03/10/21
1 item of Media coverage
Press/Media: Press / Media