Michalis Titsias
Michalis Titsias
DeepMind
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Variational learning of inducing variables in sparse Gaussian processes
M Titsias
Artificial intelligence and statistics, 567-574, 2009
9602009
Bayesian Gaussian process latent variable model
M Titsias, ND Lawrence
Proceedings of the Thirteenth International Conference on Artificial …, 2010
4152010
Doubly stochastic variational Bayes for non-conjugate inference
M Titsias, M Lázaro-Gredilla
International conference on machine learning, 1971-1979, 2014
2982014
Variational heteroscedastic Gaussian process regression
M Lázaro-Gredilla, MK Titsias
ICML, 2011
2162011
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
1882006
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
1842014
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
1722011
The generalized reparameterization gradient
FJR Ruiz, MK Titsias, DM Blei
arXiv preprint arXiv:1610.02287, 2016
1302016
Manifold relevance determination
A Damianou, C Ek, M Titsias, N Lawrence
arXiv preprint arXiv:1206.4610, 2012
1252012
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
1132010
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
1132010
Variational inference for latent variables and uncertain inputs in Gaussian processes
AC Damianou, MK Titsias, N Lawrence
1112016
The Infinite Gamma-Poisson Feature Model.
MK Titsias
NIPS 20, 1513-1520, 2007
1112007
Variational Gaussian process dynamical systems
AC Damianou, MK Titsias, ND Lawrence
arXiv preprint arXiv:1107.4985, 2011
1022011
Shared kernel models for class conditional density estimation
MK Titsias, AC Likas
IEEE Transactions on Neural Networks 12 (5), 987-997, 2001
912001
Greedy learning of multiple objects in images using robust statistics and factorial learning
CKI Williams, MK Titsias
Neural Computation 16 (5), 1039-1062, 2004
902004
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
812013
Local expectation gradients for black box variational inference
M Titsias, M Lázaro-Gredilla
Advances in neural information processing systems, 2620-2628, 2015
652015
Mixture of experts classification using a hierarchical mixture model
MK Titsias, A Likas
Neural Computation 14 (9), 2221-2244, 2002
612002
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
582016
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