Michalis Titsias
Michalis Titsias
DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Variational learning of inducing variables in sparse Gaussian processes
M Titsias
Artificial Intelligence and Statistics, 567-574, 2009
7992009
Bayesian Gaussian process latent variable model
M Titsias, ND Lawrence
Proceedings of the Thirteenth International Conference on Artificial …, 2010
3472010
Doubly stochastic variational Bayes for non-conjugate inference
M Titsias, M Lázaro-Gredilla
International conference on machine learning, 1971-1979, 2014
2632014
Variational heteroscedastic Gaussian process regression
M Lázaro-Gredilla, MK Titsias
ICML, 2011
1872011
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
1762006
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
1722014
Spike and slab variational inference for multi-task and multiple kernel learning
MK Titsias, M Lázaro-Gredilla
Advances in neural information processing systems, 2339-2347, 2011
1512011
Manifold relevance determination
A Damianou, C Ek, M Titsias, N Lawrence
arXiv preprint arXiv:1206.4610, 2012
1192012
The infinite gamma-Poisson feature model
MK Titsias
Advances in Neural Information Processing Systems, 1513-1520, 2008
1042008
The generalized reparameterization gradient
FR Ruiz, MTRC AUEB, D Blei
Advances in neural information processing systems, 460-468, 2016
1032016
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
1022010
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
1022010
Variational inference for latent variables and uncertain inputs in Gaussian processes
AC Damianou, MK Titsias, ND Lawrence
The Journal of Machine Learning Research 17 (1), 1425-1486, 2016
932016
Variational Gaussian process dynamical systems
A Damianou, MK Titsias, ND Lawrence
Advances in Neural Information Processing Systems, 2510-2518, 2011
932011
Shared kernel models for class conditional density estimation
MK Titsias, AC Likas
IEEE Transactions on Neural Networks 12 (5), 987-997, 2001
852001
Greedy learning of multiple objects in images using robust statistics and factorial learning
CKI Williams, MK Titsias
Neural Computation 16 (5), 1039-1062, 2004
842004
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
682013
Mixture of experts classification using a hierarchical mixture model
MK Titsias, A Likas
Neural Computation 14 (9), 2221-2244, 2002
582002
Local expectation gradients for black box variational inference
MTRC AUEB, M Lázaro-Gredilla
Advances in neural information processing systems, 2638-2646, 2015
562015
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
482016
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