| Variational learning of inducing variables in sparse Gaussian processes M Titsias Artificial intelligence and statistics, 567-574, 2009 | 2006 | 2009 |
| Bayesian Gaussian process latent variable model M Titsias, ND Lawrence Proceedings of the thirteenth international conference on artificial …, 2010 | 627 | 2010 |
| Doubly stochastic variational Bayes for non-conjugate inference M Titsias, M Lázaro-Gredilla International conference on machine learning, 1971-1979, 2014 | 446 | 2014 |
| Variational Heteroscedastic Gaussian Process Regression. M Lázaro-Gredilla, MK Titsias ICML, 841-848, 2011 | 336 | 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 | 263 | 2014 |
| Spike and slab variational inference for multi-task and multiple kernel learning M Titsias, M Lázaro-Gredilla Advances in neural information processing systems 24, 2011 | 243 | 2011 |
| 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 | 233 | 2006 |
| The generalized reparameterization gradient FR Ruiz, TRC AUEB, D Blei Advances in neural information processing systems 29, 2016 | 197 | 2016 |
| Variational inference for latent variables and uncertain inputs in Gaussian processes AC Damianou, MK Titsias, ND Lawrence | 191 | 2016 |
| Functional regularisation for continual learning with gaussian processes MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh arXiv preprint arXiv:1901.11356, 2019 | 172 | 2019 |
| 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 | 157 | 2010 |
| Manifold relevance determination A Damianou, C Ek, M Titsias, N Lawrence arXiv preprint arXiv:1206.4610, 2012 | 150 | 2012 |
| Variational Gaussian process dynamical systems A Damianou, M Titsias, N Lawrence Advances in neural information processing systems 24, 2011 | 138 | 2011 |
| 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 | 132 | 2013 |
| The infinite gamma-Poisson feature model M Titsias Advances in Neural Information Processing Systems 20, 2007 | 116 | 2007 |
| Greedy learning of multiple objects in images using robust statistics and factorial learning CKI Williams, MK Titsias Neural Computation 16 (5), 1039-1062, 2004 | 98 | 2004 |
| Shared kernel models for class conditional density estimation MK Titsias, AC Likas IEEE Transactions on Neural Networks 12 (5), 987-997, 2001 | 96 | 2001 |
| Local expectation gradients for black box variational inference TRC AUEB, M Lázaro-Gredilla Advances in neural information processing systems 28, 2015 | 94 | 2015 |
| 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 | 88 | 2016 |
| Variational model selection for sparse Gaussian process regression MK Titsias Report, University of Manchester, UK, 2009 | 88 | 2009 |
