Follow
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
14432009
Bayesian Gaussian process latent variable model
M Titsias, ND Lawrence
Proceedings of the thirteenth international conference on artificial …, 2010
5272010
Doubly stochastic variational Bayes for non-conjugate inference
M Titsias, M Lázaro-Gredilla
International conference on machine learning, 1971-1979, 2014
3742014
Variational heteroscedastic Gaussian process regression
M Lázaro-Gredilla, MK Titsias
ICML, 2011
2822011
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
2332014
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
2152006
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
2092011
The generalized reparameterization gradient
FR Ruiz, TRC AUEB, D Blei
Advances in neural information processing systems 29, 2016
1572016
Variational inference for latent variables and uncertain inputs in Gaussian processes
AC Damianou, MK Titsias, N Lawrence
1552016
Manifold relevance determination
A Damianou, C Ek, M Titsias, N Lawrence
arXiv preprint arXiv:1206.4610, 2012
1382012
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
1382010
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
1382010
Variational Gaussian process dynamical systems
A Damianou, M Titsias, N Lawrence
Advances in neural information processing systems 24, 2011
1202011
The infinite gamma-Poisson feature model
M Titsias
Advances in Neural Information Processing Systems 20, 2007
1142007
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
1092013
Shared kernel models for class conditional density estimation
MK Titsias, AC Likas
IEEE Transactions on Neural Networks 12 (5), 987-997, 2001
962001
Greedy learning of multiple objects in images using robust statistics and factorial learning
CKI Williams, MK Titsias
Neural Computation 16 (5), 1039-1062, 2004
952004
Local expectation gradients for black box variational inference
TRC AUEB, M Lázaro-Gredilla
Advances in neural information processing systems 28, 2015
802015
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
742016
Functional regularisation for continual learning with gaussian processes
MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh
arXiv preprint arXiv:1901.11356, 2019
722019
The system can't perform the operation now. Try again later.
Articles 1–20