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Publications
2012
M. K. Titsias, A. Honkela, N. D. Lawrence and M. Rattray.
Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model co
mparison.
BMC Systems Biology 6:53 (2012).
A. C. Damianou, C. H. Ek, M. K. Titsias and N. D. Lawrence.
Manifold Relevance Determination.
International Conference on Machine Learning (ICML), 2012.
2011
M. K. Titsias and M. Lázaro-Gredilla.
Spike and Slab Variational Inference for Multi-Task and Multiple
Kernel Learning. NIPS, 24, 2012.
[supplementary,
code].
A. C. Damianou, M. K. Titsias and N. D. Lawrence.
Variational Gaussian Process Dynamical Systems.
NIPS, 24, 2012.
M. Lázaro-Gredilla and M. K. Titsias.
Variational Heteroscedastic Gaussian Process Regression.
International Conference on Machine
Learning (ICML), 2011 Distinguished Paper Award.
M. K. Titsias.
Discussion on the paper: Riemann manifold
Langevin and Hamiltonian Monte Carlo methods,
by Girolami and Calderhead. Journal of the Royal Statistical
Society, Series B (Statistical Methodology), 73(2):201, 2011.
2010
M. K. Titsias and N. D. Lawrence.
Bayesian Gaussian Process Latent Variable Model.
Thirteenth International Conference on Artificial
Intelligence and Statistics (AISTATS), JMLR: W&CP 9, pp. 844-851, 2010.
M. Alvarez, D. Luengo, M. K. Titsias and N. D. Lawrence.
Variational inducing kernels for sparse convolved
multiple output Gaussian processes.
Thirteenth International Conference on Artificial
Intelligence and Statistics (AISTATS), JMLR: W&CP 9,
pp. 25-32, 2010.
M. K. Titsias, M. Rattray and N.D. Lawrence.
Markov chain Monte Carlo algorithms for Gaussian
processes. Chapter to appear in the book "Inference and Learning in
Dynamic Models" (Cambridge University Press), edited by
Barber, Chiappa and Cemgil.
N. D. Lawrence, M. Rattray, P. Gao and M. K. Titsias.
Gaussian processes for missing species in biochemical
systems. In N. D. Lawrence, M. Girolami, M. Rattray and
G. Sanguinetti (eds)
Learning and Inference in Computational
Systems Biology, MIT Press, Cambridge, MA.
2009
M. K. Titsias.
Variational Learning of Inducing Variables in Sparse Gaussian
Processes.
Twelfth International Conference on
Artificial Intelligence and Statistics, (AISTATS), JMLR: W&CP
5, pp. 567-574, 2009.
[technical report]
M. K. Titsias, N.D. Lawrence and M. Rattray.
Efficient Sampling for Gaussian Process Inference using Control
Variables.
Advances in Neural Information Processing
Systems, 2009.
[supplem.]
2008
M. K. Titsias.
The Infinite Gamma-Poisson Feature Model.
Advances in Neural Information Processing Systems, 2008.
2006
C. Constantinopoulos, M. K. Titsias and A. Likas.
Bayesian Feature and Model
Selection for Gaussian Mixture Models.
IEEE Trans. on Pattern Analysis and Machine Intelligence,
28(6), 1013-1018, June 2006.
M. K. Titsias and C. K.I. Williams.
Sequentially Learning of Layered Models from Video.
In C. S. J. Ponce, M. Herbert and A. Zisserman (Eds.),
Proceedings Sicily Workshop on Object Recognition, Sicily 2006.
2005
M. K. Titsias,
Unsupervised Learning of Multiple Objects in Images.
Ph.D. Thesis, School of Informatics, University of Edinburgh, 2005.
M. Allan, M. K. Titsias and C. K.I. Williams,
Fast learning of sprites using invariant features.
British Machine Vision Conference, 2005.
See videos [ videos ].
M. K. Titsias and C. K.I. Williams,
Unsupervised Learning of Multiple Aspects of Moving Objects from Video.
Advances in Informatics, PCI 2005, Volos, Greece, 2005,
M. K. Titsias and C. K. I. Williams,
Sequentially Fitting Mixtures Models using an Outlier Component.
Technical Report, 2005.
2004
M. K. Titsias and C. K. I. Williams,
Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video.
Generative-Model Based Vision Workshop, 2004.
C. K.I. Williams and M. K. Titsias,
Greedy Learning of Multiple Objects in Images using Robust Statistics and Factorial Learning.
Neural Computation, 16(5), 1039-1062, May 2004.
2003
C. K.I. Williams and M. K. Titsias,
Learning About Multiple. Objects in Images: Factorial Learning without Factorial Search.
In Advances in Neural Information Processing Systems 15, 2003.
M. K. Titsias and A. Likas,
Class conditional density estimation using mixtures with constrained component sharing.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(7), 924-928, July 2003.
Before 2003
M. K. Titsias and A. Likas,
Mixture of experts classification using a hierarchical mixture model.
Neural Computation, 14(9), 2221-2244, September 2002.
M. K. Titsias,
The Q function of the EM algorithm for hidden variable structure learning.
Technical Report, 2002.
M. K. Titsias and A. Likas,
Shared kernel models for class conditional density estimation.
IEEE Trans. on Neural Networks, 12(5), 987-997, September 2001.
M. K. Titsias and A. Likas,
A probabilistic RBF network for classification,
IJCNN (Como Italy), July 2000.
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