PhD thesis

• M. Lázaro-Gredilla (March 2010). Sparse Gaussian Processes for Large-Scale Machine Learning. PhD Thesis, Universidad Carlos III de Madrid.

Selected refereed publications

• Michalis K. Titsias and Miguel Lazaro-Gredilla. Doubly Stochastic Variational Bayes for non-Conjugate Inference. Proc of the 31st International Conference on Machine Learning (ICML 2014). Supplementary, code
• M.K. Titsias and M. Lázaro-Gredilla (2013). Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. In Advances in Neural Information Processing Systems 27 (NIPS 2013). MIT Press.
• J. Manco-Vásquez, M. Lázaro-Gredilla, D. Ramírez, J. Vía, I. Santamaría (2013). A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks. Signal Processing (In press).
• F. Pérez-Cruz, S. Van Vaerenbergh, J.J. Murillo-Fuentes, M. Lázaro-Gredilla, Ignacio Santamaria (2013). Gaussian Processes for Nonlinear Signal Processing . IEEE Signal Processing Magazine 30(4):40-50.
• M. Lázaro-Gredilla (2012). Bayesian warped Gaussian processes. In Advances in Neural Information Processing Systems 26 (NIPS 2012). MIT Press.
• S. Van Vaerenbergh, I. Santamaría and M. Lázaro-Gredilla (2011). Estimation of the Forgetting Factor in Kernel Recursive Least Squares. Machine Learning for Signal Processing (MLSP 2012).
• M. Lázaro-Gredilla, S. Van Vaerenbergh and N.D. Lawrence (2011). Overlapping Mixtures of Gaussian Processes for the Data Association Problem. Pattern Recognition, vol.10, no.4, pp.1-10, October 2011. Code.
• M. Lázaro-Gredilla, S. Van Vaerenbergh and I. Santamaría (2011). A Bayesian approach to tracking with kernel recursive least-squares. Machine Learning for Signal Processing (MLSP 2011). Code.
• M.K. Titsias and M. Lázaro-Gredilla (2011). Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning . In Advances in Neural Information Processing Systems 25 (NIPS 2011). Supplementary material, code.
• M. Lázaro-Gredilla and M.K. Titsias (2011). Variational Heteroscedastic Gaussian Process Regression. In 28th International Conference on Machine Learning (ICML 2011). Omnipress. Supplementary material, code.
• M. Lázaro-Gredilla and A.R. Figueiras-Vidal (2010). Inter-domain Gaussian processes for sparse inference using inducing features. In Advances in Neural Information Processing Systems 23 (NIPS 2009), pages 1087–1095. MIT Press.
• M. Lázaro-Gredilla, J. Arenas-García, L. A. Azpicueta-Ruiz and A. R. Figueiras- Vidal (2010). Adaptively biasing the weights of adaptive filters. IEEE Transactions on Signal Processing. 58(7):3890–3895, 2010.
• M. Lázaro-Gredilla and A.R. Figueiras-Vidal (2010). Marginalized Neural Networks Mixtures for Large-scale Regression. IEEE Transactions on Neural Networks. 21(8):1345–1351, 2010. Code.
• M. Lázaro-Gredilla, J. Quiñonero-Candela, C.E. Rasmussen, and A.R. Figueiras-Vidal (2010). Sparse Spectrum Gaussian Process Regression. Journal of Machine Learning Research, 11(Jun):1865-1881, 2010. Code.
• R. Fernández-López, M.P. Garcillán-Barcia, C. Revilla, M. Lázaro-Gredilla, L. Vielva and F. de la Cruz (2006). Dynamics of the IncW Genetic Backbone Imply General Trends in Conjugative Plasmid Evolution. FEMS Microbiology Reviews, 30 (6), pages 942–966.

Technical Reports / Preprints

• M. Lázaro-Gredilla, J. Quiñonero-Candela and A.R. Figueiras-Vidal (2007). Sparse spectral sampling Gaussian processes. Technical report, Microsoft Research.