| Variational learning of inducing variables in sparse Gaussian processes M Titsias Artificial intelligence and statistics, 567-574, 2009 | 1002 | 2009 |
| Bayesian Gaussian process latent variable model M Titsias, ND Lawrence Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 431 | 2010 |
| Doubly stochastic variational Bayes for non-conjugate inference M Titsias, M Lázaro-Gredilla International conference on machine learning, 1971-1979, 2014 | 305 | 2014 |
| Variational heteroscedastic Gaussian process regression M Lázaro-Gredilla, MK Titsias ICML, 2011 | 220 | 2011 |
| SAMHD1 is mutated recurrently in chronic lymphocytic leukemia and is involved in response to DNA damage R Clifford, T Louis, P Robbe, S Ackroyd, A Burns, AT Timbs, ... Blood, The Journal of the American Society of Hematology 123 (7), 1021-1031, 2014 | 193 | 2014 |
| Bayesian feature and model selection for Gaussian mixture models C Constantinopoulos, MK Titsias, A Likas IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (6), 1013-1018, 2006 | 189 | 2006 |
| Spike and slab variational inference for multi-task and multiple kernel learning M Titsias, M Lázaro-Gredilla Advances in neural information processing systems 24, 2339-2347, 2011 | 176 | 2011 |
| The generalized reparameterization gradient FJR Ruiz, MK Titsias, DM Blei arXiv preprint arXiv:1610.02287, 2016 | 132 | 2016 |
| Manifold relevance determination A Damianou, C Ek, M Titsias, N Lawrence arXiv preprint arXiv:1206.4610, 2012 | 129 | 2012 |
| Variational inference for latent variables and uncertain inputs in Gaussian processes AC Damianou, MK Titsias, N Lawrence | 115 | 2016 |
| Efficient multioutput Gaussian processes through variational inducing kernels M Álvarez, D Luengo, M Titsias, ND Lawrence Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 115 | 2010 |
| Efficient multioutput Gaussian processes through variational inducing kernels M Álvarez, D Luengo, M Titsias, ND Lawrence Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 115 | 2010 |
| The Infinite Gamma-Poisson Feature Model. MK Titsias NIPS 20, 1513-1520, 2007 | 112 | 2007 |
| Variational Gaussian process dynamical systems AC Damianou, MK Titsias, ND Lawrence arXiv preprint arXiv:1107.4985, 2011 | 106 | 2011 |
| Shared kernel models for class conditional density estimation MK Titsias, AC Likas IEEE Transactions on Neural Networks 12 (5), 987-997, 2001 | 92 | 2001 |
| Greedy learning of multiple objects in images using robust statistics and factorial learning CKI Williams, MK Titsias Neural Computation 16 (5), 1039-1062, 2004 | 91 | 2004 |
| Retrieval of biophysical parameters with heteroscedastic Gaussian processes M Lázaro-Gredilla, MK Titsias, J Verrelst, G Camps-Valls IEEE Geoscience and Remote Sensing Letters 11 (4), 838-842, 2013 | 85 | 2013 |
| Local expectation gradients for black box variational inference M Titsias, M Lázaro-Gredilla Advances in neural information processing systems, 2620-2628, 2015 | 65 | 2015 |
| Mixture of experts classification using a hierarchical mixture model MK Titsias, A Likas Neural Computation 14 (9), 2221-2244, 2002 | 63 | 2002 |
| First learn then earn: Optimizing mobile crowdsensing campaigns through data-driven user profiling M Karaliopoulos, I Koutsopoulos, M Titsias Proceedings of the 17th ACM international symposium on mobile ad hoc …, 2016 | 60 | 2016 |
