### 2017

T. Rukat, C. C. Holmes, M. K. Titsias, C. Yau.Bayesian Boolean Matrix Factorisation, Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.

### 2016

M. K. Titsias.One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities, NIPS, 29, 2016.

F. J. R. Ruiz, M. K. Titsias and D. M. Blei.

The Generalized Reparameterization Gradient, NIPS, 29, 2016.

M. K. Titsias, and C. Yau.

The Hamming Ball Sampler. Journal of the American Statistical Association (JASA), Theory and Methods, to appear.

F. J. R. Ruiz, M. K. Titsias and D. M. Blei.

Overdispersed Black-Box Variational Inference, Uncertainty in Artificial Intelligence (UAI), 2016. [supplementary]

M. Karaliopoulos, I. Koutsopoulos and M. K. Titsias.

First Learn then Earn: Optimizing Mobile Crowdsensing campaigns through data-driven user profiling, Proceedings of ACM International Symposium on Mobile Ad-Hoc Networking and Computing (Mobihoc), 2016.

A. Damianou*, M. K. Titsias* and N. Lawrence.

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. Journal of Machine Learning Research (JMLR), 17(42):1-62, 2016. [MATLAB code]

M. K. Titsias, C. C. Holmes and C. Yau.

Statistical Inference in Hidden Markov Models using k-segment constraints. Journal of the American Statistical Association (JASA), Theory and Methods, 111(513):200-215, 2016. [software coming soon]

*Joint first author.

**2015**

M. K. Titsias and M. Lazaro-Gredilla.

Local Expectation Gradients for Black Box Variational Inference, NIPS, 28, 2015. [supplementary] [sigmoid belief net code]

R. Bardenet* and M. K. Titsias*.

Inference for determinantal point processes without spectral knowledge. NIPS, 28, 2015.

*Joint first author.

**2014**

M. K. Titsias and C. Yau.

Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models. NIPS, 27, 2014. [supplementary] [software coming soon]

M. K. Titsias and M. Lazaro-Gredilla.

Doubly Stochastic Variational Bayes for non-Conjugate Inference. 31st International Conference on Machine Learning (ICML), Beijing, China, 2014. [supplementary] [MATLAB software]

**2013**

M. K. Titsias and M. Lazaro-Gredilla.

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. NIPS, 26, 2013. [supplementary] [MATLAB software]

R. Clifford, T. Louis, P. Robbe, S. Ackroyd, A. Burns, A. T. Timbs, G. W. Colopy, H. Dreau, F. Sigaux, J. G. Judde, M. Rotger, A. Telenti, Y-L Lin, P. Pasero, J. Maelfait, M. Titsias, D. Cohen, S. J. Henderson, M. Ross, D. Bentley, P. Hillmen, A. Pettitt, J. Rehwinkel, S. J. L. Knight, J. C. Taylor, Y. J. Crow, M. Benkirane, A. Schuh.

SAMHD1 is mutated recurrently in chronic lymphocytic leukaemia and is involved in response to DNA damage. Blood, 123(7), 1021-31, 2014.

M. Lazaro-Gredilla, M. K. Titsias, J. Verrelst and G. Camps-Valls.

Retrieval of Biophysical Parameters with Heteroscedastic Gaussian Processes. IEEE Geoscience and Remote Sensing Letters, 11(4), 838-842, 2014.

**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. Lazaro-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. Lazaro-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.