Maurizio Filippone

Journal Articles

  • B. Py, F. Wang, J. Wang, M. Filippone, and F. Ciucci. Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions. DeCarbon, 13, 100178. 2026. [link] [bib]
  • M. Rosso, J. Ylä-Jääski, Z. Shen, M. Heinonen, and M. Filippone. Gaussian Processes with Bayesian Inference of Covariate Couplings. Transactions on Machine Learning Research. 2025. [link] [code] [bib]
  • J. Wacker, M. Kanagawa, and M. Filippone. Improved Random Features for Dot Product Kernels. Journal of Machine Learning Research, 25(235), 1–75. 2024. [link] [code] [bib]
  • A. Zammit-Mangion, M.D. Kaminski, B. Tran, M. Filippone, and N. Cressie. Spatial Bayesian neural networks. Spatial Statistics, 60, 100825. 2024. [link] [code] [bib]
  • G. Franzese, S. Rossi, L. Yang, A. Finamore, D. Rossi, M. Filippone, and P. Michiardi. How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models. Entropy, 25(4). 2023. [link] [bib]
  • B. Tran, S. Rossi, D. Milios, and M. Filippone. All You Need is a Good Functional Prior for Bayesian Deep Learning. Journal of Machine Learning Research, 23(74), 1–56. 2022. [link] [code] [bib]
  • S. Marmin, and M. Filippone. Deep Gaussian Processes for Calibration of Computer Models (with Discussion). Bayesian Analysis, 17(4), 1301 – 1350. 2022. [link] [bib]
  • C. Carota, M. Filippone, and S. Polettini. Assessing Bayesian Semi-Parametric Log-Linear Models: An Application to Disclosure Risk Estimation. International Statistical Review, 90(1), 165-183. 2022. [link] [bib]
  • A. Zammit-Mangion, T.L.J. Ng, Q. Vu, and M. Filippone. Deep Compositional Spatial Models. Journal of the American Statistical Association, 117(540), 1787–1808. 2022. [link] [code] [bib]
  • R. Domingues, P. Michiardi, J. Barlet, and M. Filippone. A comparative evaluation of novelty detection algorithms for discrete sequences. Artificial Intelligence Review, 53, 3787-3812. 2020. [link] [bib]
  • M. Lorenzi, M. Filippone, G.B. Frisoni, D.C. Alexander, and S. Ourselin. Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer’s disease. NeuroImage, 190, 56–68. 2019. [link] [bib]
  • R. Domingues, P. Michiardi, J. Zouaoui, and M. Filippone. Deep Gaussian Process autoencoders for novelty detection. Machine Learning, 107(8-10), 1363–1383. 2018. [link] [bib]
  • R. Domingues, M. Filippone, P. Michiardi, and J. Zouaoui. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, 406–421. 2018. [link] [bib]
  • M. Niu, B. Macdonald, S. Rogers, M. Filippone, and D. Husmeier. Statistical inference in mechanistic models: time warping for improved gradient matching. Computational Statistics, 1–33. 2017. [link] [bib]
  • X. Xiong, V. Šmídl, and M. Filippone. Adaptive multiple importance sampling for Gaussian processes. Journal of Statistical Computation and Simulation, 87(8), 1644-1665. 2017. [link] [bib]
  • B. Macdonald, M. Niu, S. Rogers, M. Filippone, and D. Husmeier. Approximate parameter inference in systems biology using gradient matching: a comparative evaluation. BioMedical Engineering OnLine, 15(Suppl 1), 80. 2016. [link] [bib]
  • J.M. Rondina, M. Filippone, M. Girolami, and N.S. Ward. Decoding post-stroke motor function from structural brain imaging. NeuroImage: Clinical, 12, 372-380. 2016. [link] [bib]
  • C. Carota, M. Filippone, R. Leombruni, and S. Polettini. Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models. Annals of Applied Statistics, 9(1), 525-546. 2015. [link] [bib]
  • M. Dell’Amico, M. Filippone, P. Michiardi, and Y. Roudier. On User Availability Prediction and Network Applications. IEEE/ACM Transactions on Networking, 23(4), 1300-1313. 2015. [link] [bib]
  • M. Filippone, and M. Girolami. Pseudo-Marginal Bayesian Inference for Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11), 2214-2226. 2014. [link] [bib]
  • S. Kim, F. Valente, M. Filippone, and A. Vinciarelli. Predicting Continuous Conflict Perception with Bayesian Gaussian Processes. IEEE Transactions on Affective Computing, 5(2), 187-200. 2014. [link] [bib]
  • A.F. Marquand, M. Filippone, J. Ashburner, M. Girolami, J. Mourão-Miranda, G.J. Barker, S.C… Williams, P.N. Leigh, and C.R.V. Blain. Automated, High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach. PLoS ONE, 8(7), e69237+. 2013. [link] [bib]
  • M. Filippone, M. Zhong, and M. Girolami. A comparative evaluation of stochastic-based inference methods for Gaussian process models. Machine Learning, 93(1), 93-114. 2013. [link] [bib]
  • Y. Zhao, J. Kim, and M. Filippone. Aggregation Algorithm Towards Large-Scale Boolean Network Analysis. IEEE Transactions on Automatic Control, 58(8), 1976-1985. 2013. [link] [bib]
  • M. Filippone, A.F. Marquand, C.R.V. Blain, S.C… Williams, J. Mourão-Miranda, and M. Girolami. Probabilistic Prediction of Neurological Disorders with a Statistical Assessment of Neuroimaging Data Modalities. Annals of Applied Statistics, 6(4), 1883-1905. 2012. [link] [bib]
  • L. Mohamed, B. Calderhead, M. Filippone, M. Christie, and M. Girolami. Population MCMC methods for history matching and uncertainty quantification. Computational Geosciences, 16(2), 423-436. 2012. [link] [bib]
  • M. Filippone, and G. Sanguinetti. Approximate inference of the bandwidth in multivariate kernel density estimation. Computational Statistics & Data Analysis, 55(12), 3104-3122. 2011. [link] [bib]
  • M. Filippone, and G. Sanguinetti. A Perturbative Approach to Novelty Detection in Autoregressive Models. IEEE Transactions on Signal Processing, 59(3), 1027-1036. 2011. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Simulated annealing for supervised gene selection. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 15, 1471-1482. 2011. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Applying the Possibilistic C-Means Algorithm in Kernel-Induced Spaces. IEEE Transactions on Fuzzy Systems, 18(3), 572-584. 2010. [link] [bib]
  • M. Filippone, and G. Sanguinetti. Information Theoretic Novelty Detection. Pattern Recognition, 43(3), 805-814. 2010. [link] [bib]
  • M. Filippone. Dealing with Non-Metric Dissimilarities in Fuzzy Central Clustering Algorithms. International Journal of Approximate Reasoning, 50(2), 363-384. 2009. [link] [bib]
  • F. Camastra, and M. Filippone. A comparative evaluation of Nonlinear Dynamics Methods for Time Series Prediction. Neural Computing and Applications, 18(8), 1021-1029. 2009. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Clustering in the Membership Embedding Space. International Journal of Knowledge Engineering and Soft Data Paradigms, 4(1), 363-375. 2009. [bib]
  • S. Rovetta, F. Masulli, and M. Filippone. Soft Ranking in Clustering. Neurocomputing, 72(7-9), 2028-2031. 2009. [link] [bib]
  • M. Filippone, F. Camastra, F. Masulli, and S. Rovetta. A Survey of Kernel and Spectral Methods for Clustering. Pattern Recognition, 41(1), 176-190. 2008. [link] [bib]

Conference Papers

  • A. Benechehab, V. Feofanov, G. Paolo, A. Thomas, M. Filippone, and B. Kégl. AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting. Proceedings of the 42nd International Conference on Machine Learning. 2025. [link] [code] [bib]
  • M. Heinonen, B. Tran, M. Kampffmeyer, and M. Filippone. Robust Classification by Coupling Data Mollification with Label Smoothing. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. 2025. [link] [code] [bib]
  • A. Lhéritier, and M. Filippone. Unconditionally Calibrated Priors for Beta Mixture Density Networks. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. 2025. [link] [code] [bib]
  • A. Benechehab, Y.A.E. Hili, A. Odonnat, O. Zekri, A. Thomas, G. Paolo, M. Filippone, I. Redko, and B. Kégl. Zero-shot Model-based Reinforcement Learning using Large Language Models. The Thirteenth International Conference on Learning Representations. 2025. [link] [code] [bib]
  • L. Nepote, A. Lhéritier, N. Bondoux, M. Kountouris, and M. Filippone. Variational Inference for Quantum HyperNetworks. International Joint Conference on Neural Networks, IJCNN 2025, Rome, Italy, June 30 - July 5, 2025. 2025. [link] [bib]
  • T. Papamarkou, M. Skoularidou, K. Palla, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, J.M. Hernández-Lobato, A. Hubin, A. Immer, T. Karaletsos, M.E. Khan, A. Kristiadi, Y. Li, S. Mandt, C. Nemeth, M.A. Osborne, T.G.J. Rudner, D. Rügamer, Y.W. Teh, M. Welling, A.G. Wilson, and R. Zhang. Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. Proceedings of the 41st International Conference on Machine Learning. 2024. [link] [bib]
  • B. Tran, B. Shahbaba, S. Mandt, and M. Filippone. Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. Proceedings of the 40th International Conference on Machine Learning. 2023. [link] [code] [bib]
  • B. Tran, G. Franzese, P. Michiardi, and M. Filippone. One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models. Advances in Neural Information Processing Systems. 2023. [link] [code] [bib]
  • G. Franzese, G. Corallo, S. Rossi, M. Heinonen, M. Filippone, and P. Michiardi. Continuous-Time Functional Diffusion Processes. Advances in Neural Information Processing Systems. 2023. [link] [code] [bib]
  • J. Wacker, R. Ohana, and M. Filippone. Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. 2023. [link] [code] [bib]
  • G. Franzese, D. Milios, M. Filippone, and P. Michiardi. Revisiting the Effects of Stochasticity for Hamiltonian Samplers. Proceedings of the 39th International Conference on Machine Learning. 2022. [link] [bib]
  • B. Tran, S. Rossi, D. Milios, P. Michiardi, E. Bonilla, and M. Filippone. Model Selection for Bayesian Autoencoders. Advances in Neural Information Processing Systems. 2021. [link] [code] [bib]
  • G. Tran, D. Milios, P. Michiardi, and M. Filippone. Sparse within Sparse Gaussian Processes using Neighbor Information. Proceedings of the 38th International Conference on Machine Learning. 2021. [link] [code] [bib]
  • G. Mita, M. Filippone, and P. Michiardi. An Identifiable Double VAE For Disentangled Representations. Proceedings of the 38th International Conference on Machine Learning. 2021. [link] [code] [bib]
  • S. Rossi, M. Heinonen, E. Bonilla, Z. Shen, and M. Filippone. Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. 2021. [link] [code] [bib]
  • S. Rossi, S. Marmin, and M. Filippone. Walsh-Hadamard Variational Inference for Bayesian Deep Learning. Advances in Neural Information Processing Systems. 2020. [link] [code] [bib]
  • G. Mita, P. Papotti, M. Filippone, and P. Michiardi. LIBRE: Learning Interpretable Boolean Rule Ensembles. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. 2020. [link] [code] [bib]
  • S. Rossi, S. Marmin, and M. Filippone. Efficient Approximate Inference with Walsh-Hadamard Variational Inference. Bayesian Deep Learning Workshop, NeurIPS. 2019. [link] [code] [bib]
  • C. Nemeth, F. Lindsten, M. Filippone, and J. Hensman. Pseudo-Extended Markov chain Monte Carlo. Advances in Neural Information Processing Systems. 2019. [link] [code] [bib]
  • S. Rossi, P. Michiardi, and M. Filippone. Good Initializations of Variational Bayes for Deep Models. Proceedings of the 36th International Conference on Machine Learning. 2019. [link] [code] [bib]
  • G. Tran, E.V. Bonilla, J. Cunningham, P. Michiardi, and M. Filippone. Calibrating Deep Convolutional Gaussian Processes. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. 2019. [link] [code] [bib]
  • D. Nguyen, M. Filippone, and P. Michiardi. Exact Gaussian process regression with distributed computations. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019, Limassol, Cyprus, April 8-12, 2019. 2019. [link] [bib]
  • D. Milios, R. Camoriano, P. Michiardi, L. Rosasco, and M. Filippone. Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. Advances in Neural Information Processing Systems. 2018. [link] [code] [bib]
  • M. Lorenzi, and M. Filippone. Constraining the Dynamics of Deep Probabilistic Models. Proceedings of the 35th International Conference on Machine Learning. 2018. [link] [code] [bib]
  • J.K. Fitzsimons, D. Granziol, K. Cutajar, M.A. Osborne, M. Filippone, and S.J. Roberts. Entropic Trace Estimates for Log Determinants. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I. 2017. [link] [code] [bib]
  • J.K. Fitzsimons, K. Cutajar, M. Filippone, M.A. Osborne, and S.J. Roberts. Bayesian Inference of Log Determinants. Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017. 2017. [link] [bib]
  • K. Krauth, E.V. Bonilla, K. Cutajar, and M. Filippone. AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models. Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017. 2017. [link] [code] [bib]
  • K. Cutajar, E.V. Bonilla, P. Michiardi, and M. Filippone. Random Feature Expansions for Deep Gaussian Processes. Proceedings of the 34th International Conference on Machine Learning. 2017. [link] [code] [bib]
  • Y. Han, and M. Filippone. Mini-batch spectral clustering. 2017 International Joint Conference on Neural Networks (IJCNN). 2017. [code] [bib]
  • K. Cutajar, E.V. Bonilla, P. Michiardi, and M. Filippone. Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels. Bayesian Deep Learning Workshop, NIPS. 2016. [link] [code] [bib]
  • X. Xiong, M. Filippone, and A. Vinciarelli. Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions. ACM Multimedia. 2016. [bib]
  • K. Cutajar, M. Osborne, J. Cunningham, and M. Filippone. Preconditioning Kernel Matrices. Proceedings of The 33rd International Conference on Machine Learning. 2016. [link] [code] [bib]
  • M. Niu, S. Rogers, M. Filippone, and D. Husmeier. Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. Proceedings of The 33rd International Conference on Machine Learning. 2016. [link] [bib]
  • J. Hensman, A.G. Matthews, M. Filippone, and Z. Ghahramani. MCMC for Variationally Sparse Gaussian Processes. Advances in Neural Information Processing Systems. 2015. [link] [code] [bib]
  • M. Dell’Amico, and M. Filippone. Monte Carlo Strength Evaluation: Fast and Reliable Password Checking. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 2015. [link] [code] [bib]
  • M. Filippone, and R. Engler. Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE). Proceedings of the 32nd International Conference on Machine Learning. 2015. [link] [bib]
  • M. Filippone. Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC. 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, August 24-28, 2014. 2014. [link] [bib]
  • A.D. O’Harney, A. Marquand, K. Rubia, K. Chantiluke, A.B. Smith, A. Cubillo, C. Blain, and M. Filippone. Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data. 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, August 24-28, 2014. 2014. [link] [bib]
  • F. Dondelinger, D. Husmeier, S. Rogers, and M. Filippone. ODE parameter inference using adaptive gradient matching with Gaussian processes. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics. 2013. [link] [code] [bib]
  • S. Kim, M. Filippone, F. Valente, and A. Vinciarelli. Predicting the conflict level in television political debates: an approach based on crowdsourcing, nonverbal communication and Gaussian processes. Proceedings of the 20th ACM Multimedia Conference, MM ‘12, Nara, Japan, October 29 - November 02, 2012. 2012. [link] [bib]
  • G. Mohammadi, A. Origlia, M. Filippone, and A. Vinciarelli. From speech to personality: mapping voice quality and intonation into personality differences. Proceedings of the 20th ACM Multimedia Conference, MM ‘12, Nara, Japan, October 29 - November 02, 2012. 2012. [link] [bib]
  • D. Barbará, C. Domeniconi, Z. Duric, M. Filippone, R. Mansfield, and E. Lawson. Detecting Suspicious Behavior in Surveillance Images. Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15-19, 2008, Pisa, Italy. 2008. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Stability and Performances in Biclustering Algorithms. Computational Intelligence Methods for Bioinformatics and Biostatistics, 5th International Meeting, CIBB 2008, Vietri sul Mare, Italy, October 3-4, 2008, Revised Selected Papers. 2008. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. An Experimental Comparison of Kernel Clustering Methods. New Directions in Neural Networks - 18th Italian Workshop on Neural Networks: WIRN 2008, Vietri sul Mare, Italy, May 22-24, 2008, Revised Selected Papers. 2008. [link] [bib]
  • F. Camastra, and M. Filippone. SVM-Based Time Series Prediction with Nonlinear Dynamics Methods. Knowledge-Based Intelligent Information and Engineering Systems, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007, Proceedings, Part III. 2007. [link] [bib]
  • S. Rovetta, F. Masulli, and M. Filippone. Membership Embedding Space Approach and Spectral Clustering. Knowledge-Based Intelligent Information and Engineering Systems, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007, Proceedings, Part III. 2007. [link] [bib]
  • E. Canestrelli, P. Canestrelli, M. Corazza, M. Filippone, S. Giove, and F. Masulli. Local Learning of Tide Level Time Series using a Fuzzy Approach. Proceedings of the International Joint Conference on Neural Networks, IJCNN 2007, Celebrating 20 years of neural networks, Orlando, Florida, USA, August 12-17, 2007. 2007. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Possibilistic Clustering in Feature Space. Applications of Fuzzy Sets Theory, 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings. 2007. [link] [bib]
  • M. Filippone, F. Masulli, S. Rovetta, S. Mitra, and H. Banka. Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis. Computational Methods in Systems Biology, International Conference, CMSB 2006, Trento, Italy, October 18-19, 2006, Proceedings. 2006. [link] [bib]
  • M. Filippone, F. Masulli, S. Rovetta, and S. Constantinescu. Input Selection with Mixed Data Sets: A Simulated Annealing Wrapper Approach. CISI 06 - Conferenza Italiana Sistemi Intelligenti. 2006. [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Gene Expression Data Analysis in the Membership Embedding Space: A Constructive Approach.. CIBB 2006 - Third International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. 2006. [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Supervised Classification and Gene Selection Using Simulated Annealing. Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16-21 July 2006. 2006. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. Unsupervised Gene Selection and Clustering Using Simulated Annealing.. Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, Crema, Italy, September 15-17, 2005, Revised Selected Papers. 2005. [link] [bib]
  • F. Masulli, S. Rovetta, and M. Filippone. Clustering Genomic Data in the Membership Embedding Space. CI-BIO - Workshop on Computational Intelligence Approaches for the Analysis of Bioinformatics Data. 2005. [bib]
  • S. Rovetta, F. Masulli, and M. Filippone. Soft Rank Clustering. Neural Nets, 16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005, Vietri sul Mare, Italy, June 8-11, 2005, Revised Selected Papers. 2005. [link] [bib]
  • M. Filippone, F. Masulli, and S. Rovetta. ERAF: a R Package for Regression and Forecasting. Biological and Artificial Intelligence Environments. 2004. [bib]

Discussions

  • S. Rossi, C. Rusu, L.A. Rosasco, and M. Filippone. Contributed discussion on “A Bayesian conjugate gradient method”. Bayesian Analysis, 14(3), 2019. 2019. [link] [bib]
  • M. Filippone, A. Mira, and M. Girolami. Discussion of the paper: ‘‘Sampling schemes for generalized linear Dirichlet process random effects models’’ by M. Kyung, J. Gill, and G. Casella. Statistical Methods & Applications, 20, 295-297. 2011. [link] [bib]
  • M. Filippone. Discussion of the paper ‘‘Riemann manifold Langevin and Hamiltonian Monte Carlo methods’’ by Mark Girolami and Ben Calderhead. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 73(2), 164-165. 2011. [link] [bib]
  • V. Stathopoulos, and M. Filippone. Discussion of the paper ‘‘Riemann manifold Langevin and Hamiltonian Monte Carlo methods’’ by Mark Girolami and Ben Calderhead. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 73(2), 167-168. 2011. [link] [bib]

Theses

  • M. Filippone. Central Clustering in Kernel-Induced Spaces. Ph.D. thesis, University of Genova. 2008. [bib]
  • M. Filippone. Metodi di Ensemble per la Previsione di Serie Storiche. M.Sc. thesis, University of Genova. 2004. [bib]