| A survey of kernel and spectral methods for clustering M Filippone, F Camastra, F Masulli, S Rovetta Pattern recognition 41 (1), 176-190, 2008 | 1130 | 2008 |
| A comparative evaluation of outlier detection algorithms: Experiments and analyses R Domingues, M Filippone, P Michiardi, J Zouaoui Pattern recognition 74, 406-421, 2018 | 570 | 2018 |
| Random feature expansions for deep Gaussian processes K Cutajar, EV Bonilla, P Michiardi, M Filippone Proceedings of the 35th International Conference on Machine Learning 70, 884 …, 2017 | 191 | 2017 |
| MCMC for variationally sparse Gaussian processes J Hensman, AG Matthews, M Filippone, Z Ghahramani Advances in neural information processing systems 28, 2015 | 176 | 2015 |
| Aggregation algorithm towards large-scale Boolean network analysis Y Zhao, J Kim, M Filippone IEEE Transactions on Automatic Control 58 (8), 1976-1985, 2013 | 162 | 2013 |
| ODE parameter inference using adaptive gradient matching with Gaussian processes F Dondelinger, D Husmeier, S Rogers, M Filippone Artificial intelligence and statistics, 216-228, 2013 | 160 | 2013 |
| Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease M Lorenzi, M Filippone, GB Frisoni, DC Alexander, S Ourselin, ... NeuroImage 190, 56-68, 2019 | 144 | 2019 |
| Monte Carlo strength evaluation: Fast and reliable password checking M Dell'Amico, M Filippone Proceedings of the 22nd ACM SIGSAC conference on computer and communications …, 2015 | 129 | 2015 |
| Decoding post-stroke motor function from structural brain imaging JM Rondina, M Filippone, M Girolami, NS Ward NeuroImage: Clinical 12, 372-380, 2016 | 112 | 2016 |
| Dirichlet-based gaussian processes for large-scale calibrated classification D Milios, R Camoriano, P Michiardi, L Rosasco, M Filippone Advances in Neural Information Processing Systems 31, 2018 | 106 | 2018 |
| Preconditioning kernel matrices K Cutajar, MA Osborne, JP Cunningham, M Filippone Proceedings of the 33rd International Conference on Machine Learning, 2529-2538, 2016 | 99 | 2016 |
| All you need is a good functional prior for Bayesian deep learning BH Tran, S Rossi, D Milios, M Filippone Journal of Machine Learning Research 23 (74), 1-56, 2022 | 87 | 2022 |
| Pseudo-marginal Bayesian inference for Gaussian processes M Filippone, M Girolami IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (11), 2214 …, 2014 | 81 | 2014 |
| Deep compositional spatial models A Zammit-Mangion, TLJ Ng, Q Vu, M Filippone Journal of the American Statistical Association 117 (540), 1787-1808, 2022 | 76 | 2022 |
| A comparative evaluation of stochastic-based inference methods for Gaussian process models M Filippone, M Zhong, M Girolami Machine Learning 93, 93-114, 2013 | 71 | 2013 |
| AutoGP: Exploring the capabilities and limitations of Gaussian process models K Krauth, EV Bonilla, K Cutajar, M Filippone arXiv preprint arXiv:1610.05392, 2016 | 68 | 2016 |
| Information theoretic novelty detection M Filippone, G Sanguinetti Pattern Recognition 43 (3), 805-814, 2010 | 58 | 2010 |
| Position: Bayesian deep learning is needed in the age of large-scale AI T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ... arXiv preprint arXiv:2402.00809, 2024 | 54* | 2024 |
| Predicting Continuous Conflict Perception with Bayesian Gaussian Processes S Kim, F Valente, M Filippone, A Vinciarelli IEEE Transactions on Affective Computing 5 (2), 187-200, 2014 | 54 | 2014 |
| From speech to personality: Mapping voice quality and intonation into personality differences G Mohammadi, A Origlia, M Filippone, A Vinciarelli Proceedings of the 20th ACM international conference on Multimedia, 789-792, 2012 | 54 | 2012 |
Pietro MichiardiEURECOMVerified email at eurecom.fr
