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Michalis Titsias
Michalis Titsias
DeepMind
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Title
Cited by
Cited by
Year
Variational learning of inducing variables in sparse Gaussian processes
M Titsias
Artificial intelligence and statistics, 567-574, 2009
19032009
Bayesian Gaussian process latent variable model
M Titsias, ND Lawrence
Proceedings of the thirteenth international conference on artificial …, 2010
6152010
Doubly stochastic variational Bayes for non-conjugate inference
M Titsias, M Lázaro-Gredilla
International conference on machine learning, 1971-1979, 2014
4292014
Variational Heteroscedastic Gaussian Process Regression.
M Lázaro-Gredilla, MK Titsias
ICML, 841-848, 2011
3282011
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
2602014
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
2372011
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
2332006
The generalized reparameterization gradient
FR Ruiz, TRC AUEB, D Blei
Advances in neural information processing systems 29, 2016
1922016
Variational inference for latent variables and uncertain inputs in Gaussian processes
AC Damianou, MK Titsias, ND Lawrence
1882016
Functional regularisation for continual learning with gaussian processes
MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh
arXiv preprint arXiv:1901.11356, 2019
1602019
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
1512010
Manifold relevance determination
A Damianou, C Ek, M Titsias, N Lawrence
arXiv preprint arXiv:1206.4610, 2012
1502012
Variational Gaussian process dynamical systems
A Damianou, M Titsias, N Lawrence
Advances in neural information processing systems 24, 2011
1342011
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
1262013
The infinite gamma-Poisson feature model
M Titsias
Advances in Neural Information Processing Systems 20, 2007
1192007
Greedy learning of multiple objects in images using robust statistics and factorial learning
CKI Williams, MK Titsias
Neural Computation 16 (5), 1039-1062, 2004
972004
Shared kernel models for class conditional density estimation
MK Titsias, AC Likas
IEEE Transactions on Neural Networks 12 (5), 987-997, 2001
962001
Local expectation gradients for black box variational inference
TRC AUEB, M Lázaro-Gredilla
Advances in neural information processing systems 28, 2015
932015
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
862016
Variational model selection for sparse Gaussian process regression
MK Titsias
Report, University of Manchester, UK, 2009
842009
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