Michalis Titsias
Michalis Titsias
DeepMind
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TitleCited byYear
Variational learning of inducing variables in sparse Gaussian processes
M Titsias
Artificial Intelligence and Statistics, 567-574, 2009
6462009
Bayesian Gaussian process latent variable model
M Titsias, ND Lawrence
Proceedings of the Thirteenth International Conference on Artificial …, 2010
3022010
Doubly stochastic variational Bayes for non-conjugate inference
M Titsias, M Lázaro-Gredilla
International conference on machine learning, 1971-1979, 2014
2182014
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
1652006
Variational Heteroscedastic Gaussian Process Regression.
M Lázaro-Gredilla, MK Titsias
ICML, 841-848, 2011
1582011
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, GW Colopy, ...
Blood 123 (7), 1021-1031, 2014
1462014
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
1332011
Manifold relevance determination
A Damianou, C Ek, M Titsias, N Lawrence
arXiv preprint arXiv:1206.4610, 2012
1132012
The infinite gamma-Poisson feature model
MK Titsias
Advances in Neural Information Processing Systems, 1513-1520, 2008
922008
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
882010
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
882010
The generalized reparameterization gradient
FR Ruiz, MTRC AUEB, D Blei
Advances in neural information processing systems, 460-468, 2016
852016
Shared kernel models for class conditional density estimation
MK Titsias, AC Likas
IEEE Transactions on Neural Networks 12 (5), 987-997, 2001
832001
Variational Gaussian process dynamical systems
A Damianou, MK Titsias, ND Lawrence
Advances in Neural Information Processing Systems, 2510-2518, 2011
792011
Greedy learning of multiple objects in images using robust statistics and factorial learning
CKI Williams, MK Titsias
Neural Computation 16 (5), 1039-1062, 2004
792004
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
782016
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
582013
Mixture of experts classification using a hierarchical mixture model
MK Titsias, A Likas
Neural Computation 14 (9), 2221-2244, 2002
542002
Local expectation gradients for black box variational inference
MTRC AUEB, M Lázaro-Gredilla
Advances in neural information processing systems, 2638-2646, 2015
502015
Efficient sampling for Gaussian process inference using control variables
ND Lawrence, M Rattray, MK Titsias
Advances in Neural Information Processing Systems, 1681-1688, 2009
412009
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Articles 1–20