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. [ bib | pdf | http ]
M. Dell'Amico, M. Filippone, P. Michiardi, and Y. Roudier. On user availability prediction and network applications. IEEE Transactions on Networking, 2014. [ bib | pdf | http ]
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. [ bib | pdf | http ]
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. [ bib | pdf | http ]
A. F. Marquand, M. Filippone, J. Ashburner, M. Girolami, J. Mourão-Miranda, G. J. Barker, S. C. R. 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. [ bib | supplementary material | pdf | http ]
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. [ bib | pdf | http ]
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. [ bib | pdf | http ]
M. Filippone, A. F. Marquand, C. R. V. Blain, S. C. R. 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. [ bib | pdf | http ]
M. Filippone and G. Sanguinetti. Approximate inference of the bandwidth in multivariate kernel density estimation. Computational Statistics & Data Analysis, 55(12):3104-3122, 2011. [ bib | pdf | http ]
M. Filippone and G. Sanguinetti. A perturbative approach to novelty detection in autoregressive models. IEEE Transactions on Signal Processing, 59(3):1027-1036, 2011. [ bib | pdf | http ]
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. [ bib | http ]
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, June 2010. [ bib | pdf | http ]
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. [ bib | http ]
M. Filippone and G. Sanguinetti. Information theoretic novelty detection. Pattern Recognition, 43(3):805-814, March 2010. [ bib | pdf | http ]
M. Filippone. Dealing with non-metric dissimilarities in fuzzy central clustering algorithms. International Journal of Approximate Reasoning, 50(2):363-384, February 2009. [ bib | pdf | http ]
F. Camastra and M. Filippone. A comparative evaluation of nonlinear dynamics methods for time series prediction. Neural Computing and Applications, 18(8):1021-1029, November 2009. [ bib | pdf | http ]
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, March 2009. [ bib | pdf | http ]
M. Filippone, F. Camastra, F. Masulli, and S. Rovetta. A survey of kernel and spectral methods for clustering. Pattern Recognition, 41(1):176-190, January 2008. [ bib | Best Paper Award | pdf | http ]
M. Dell'Amico and M. Filippone. Monte Carlo strength evaluation: Fast and reliable password checking. In Proceedings of the 22nd ACM Conference on Computer and Communications Security, 2015. [ bib | pdf ]
M. Filippone and R. Engler. Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE). In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, July 6-11, 2015, 2015. [ bib | pdf ]
M. Filippone. Bayesian inference for Gaussian process classifiers with annealing and pseudo-marginal MCMC. In 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, August 24-28, 2014, pages 614-619. IEEE, 2014. [ bib | pdf | http ]
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. In 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, August 24-28, 2014, pages 3185-3190. IEEE, 2014. [ bib | pdf | http ]
F. Dondelinger, M. Filippone, S. Rogers, and D. Husmeier. ODE parameter inference using adaptive gradient matching with Gaussian processes. In AISTATS, 2013. [ bib | pdf ]
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. In N. Babaguchi, K. Aizawa, J. R. Smith, S. Satoh, T. Plagemann, X.-S. Hua, and R. Yan, editors, ACM Multimedia, pages 793-796. ACM, 2012. [ bib | pdf ]
G. Mohammadi, A. Origlia, M. Filippone, and A. Vinciarelli. From speech to personality: mapping voice quality and intonation into personality differences. In N. Babaguchi, K. Aizawa, J. R. Smith, S. Satoh, T. Plagemann, X.-S. Hua, and R. Yan, editors, ACM Multimedia, pages 789-792. ACM, 2012. [ bib | pdf ]
D. Barbará, C. Domeniconi, Z. Duric, M. Filippone, R. Mansfield, and E. Lawson. Detecting suspicious behavior in surveillance images. In ICDM Workshops, pages 891-900. IEEE Computer Society, 2008. [ bib | pdf | http ]
M. Filippone, F. Masulli, and S. Rovetta. Stability and performances in biclustering algorithms. In F. Masulli, R. Tagliaferri, and G. Verkhivker, editors, CIBB, volume 5488 of Lecture Notes in Computer Science, pages 91-101. Springer, 2008. [ bib ]
M. Filippone, F. Masulli, and S. Rovetta. An experimental comparison of kernel clustering methods. In B. Apolloni, S. Bassis, and M. Marinaro, editors, WIRN, volume 193 of Frontiers in Artificial Intelligence and Applications, pages 118-126. IOS Press, 2008. [ bib ]
F. Camastra and M. Filippone. SVM-based time series prediction with nonlinear dynamics methods. In B. Apolloni, R. J. Howlett, and L. C. Jain, editors, KES (3), volume 4694 of Lecture Notes in Computer Science, pages 300-307. Springer, 2007. [ bib | pdf | http ]
S. Rovetta, F. Masulli, and M. Filippone. Membership embedding space approach and spectral clustering. In B. Apolloni, R. J. Howlett, and L. C. Jain, editors, KES (3), volume 4694 of Lecture Notes in Computer Science, pages 901-908. Springer, 2007. [ bib | pdf | http ]
E. Canestrelli, P. Canestrelli, M. Corazza, M. Filippone, S. Giove, and F. Masulli. Local learning of tide level time series using a fuzzy approach. In IJCNN, pages 1813-1818. IEEE, 2007. [ bib | pdf | http ]
M. Filippone, F. Masulli, and S. Rovetta. Possibilistic clustering in feature space. In F. Masulli, S. Mitra, and G. Pasi, editors, WILF, volume 4578 of Lecture Notes in Computer Science, pages 219-226. Springer, 2007. [ bib | pdf | http ]
M. Filippone, F. Masulli, S. Rovetta, S. Mitra, and H. Banka. Possibilistic approach to biclustering: An application to oligonucleotide microarray data analysis. In C. Priami, editor, CMSB, volume 4210 of Lecture Notes in Computer Science, pages 312-322. Springer, 2006. [ bib | pdf | http ]
M. Filippone, F. Masulli, S. Rovetta, and S.-P. Constantinescu. Input selection with mixed data sets: A simulated annealing wrapper approach. In CISI 06 - Conferenza Italiana Sistemi Intelligenti, Ancona - Italy, 27-29 September 2006. [ bib | pdf ]
M. Filippone, F. Masulli, and S. Rovetta. Gene expression data analysis in the membership embedding space: A constructive approach. In CIBB 2006 - Third International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Genova - Italy, 29-31 August 2006. [ bib | pdf ]
M. Filippone, F. Masulli, and S. Rovetta. Supervised classification and gene selection using simulated annealing. In IJCNN, pages 3566-3571. IEEE, 2006. [ bib | pdf | http ]
M. Filippone, F. Masulli, and S. Rovetta. Unsupervised gene selection and clustering using simulated annealing. In I. Bloch, A. Petrosino, and A. Tettamanzi, editors, WILF, volume 3849 of Lecture Notes in Computer Science, pages 229-235. Springer, 2005. [ bib | pdf | http ]
F. Masulli, S. Rovetta, and M. Filippone. Clustering genomic data in the membership embedding space. In CI-BIO - Workshop on Computational Intelligence Approaches for the Analysis of Bioinformatics Data, Montreal - Canada, 5 August 2005. [ bib | pdf ]
S. Rovetta, F. Masulli, and M. Filippone. Soft rank clustering. In B. Apolloni, M. Marinaro, G. Nicosia, and R. Tagliaferri, editors, WIRN/NAIS, volume 3931 of Lecture Notes in Computer Science, pages 207-213. Springer, 2005. [ bib | pdf | http ]
M. Filippone, F. Masulli, and S. Rovetta. ERAF: a R package for regression and forecasting. In Biological and Artificial Intelligence Environments, pages 165-173, Secaucus, NJ, USA, 2005. Springer-Verlag New York, Inc. [ bib | pdf ]
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. [ bib | pdf | http ]
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. [ bib | pdf | http ]
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, March 2011. [ bib | pdf | http ]
J. Hensman, A. G. de G. Matthews, M. Filippone, and Z. Ghahramani. MCMC for variationally sparse Gaussian processes, 2015. arXiv:1506.04000. [ bib | pdf | http ]
M. Filippone and R. Engler. Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE), 2015. arXiv:1501.05427. [ bib | pdf | http ]
M. Filippone. Bayesian inference for Gaussian process classifiers with annealing and pseudo-marginal MCMC, 2013. arXiv:1311.7320. [ bib | pdf | http ]
M. Filippone and M. Girolami. Pseudo-marginal Bayesian inference for Gaussian processes, 2013. arXiv:1310.0740. [ bib | pdf | http ]
C. Carota, M. Filippone, R. Leombruni, and S. Polettini. Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models, June 2013. arXiv:1306.5995. [ bib | pdf | http ]
M. Filippone, M. Zhong, and M. Girolami. On the fully Bayesian treatment of latent Gaussian models using stochastic simulations. Technical Report TR-2012-329, School of Computing Science, University of Glasgow, February 2012. [ bib | pdf ]
M. Filippone and G. Sanguinetti. Novelty detection in autoregressive models using information theoretic measures. Technical Report CS-09-06, Department of Computer Science, University of Sheffield, July 2009. [ bib | pdf ]
M. Filippone and G. Sanguinetti. Information theoretic novelty detection. Technical Report CS-09-02, Department of Computer Science, University of Sheffield, February 2009. [ bib | pdf ]
M. Filippone. Fuzzy clustering of patterns represented by pairwise dissimilarities. Technical Report ISE-TR-07-05, Department of Information and Software Engineering, George Mason University, October 2007. [ bib | pdf ]
M. Filippone, F. Camastra, F. Masulli, and S. Rovetta. A survey of kernel and spectral methods for clustering. Technical Report DISI-TR-06-19, Department of Computer and Information Sciences at the University of Genova, Italy, 18th October 2006. [ bib ]
M. Filippone, F. Masulli, and S. Rovetta. A wrapper approach to supervised input selection using simulated annealing. Technical Report DISI-TR-06-10, Department of Computer and Information Sciences at the University of Genova, Italy, 12th June 2006. [ bib | pdf ]