S. Sivakumaran, F. V. Agakov, E. Theodoratou, J. G. Prendergast, L. Zgaga, T. Manolio, I. Rudan, P. McKeigue, J. F. Wilson, H. Campbell.
Abundant Pleiotropy in Human Complex Diseases and Traits.
Am. J. of Hum. Gen., 89(5), 2011, 12p. [pdf]
F. V. Agakov, P. McKeigue, J. Krohn, J. Flint.
Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions.
Accepted to J of Sys. Cyb. and Inf., 8p. [pdf] Short version appeared in BMIC 2010. The abstract appeared in European Mathematical Genetics Meeting (EMGM) 2010.
F. V. Agakov, P. McKeigue, J. Krohn, A. Storkey.
Sparse Instrumental Variables (SPIV) for Genome-Wide Studies.
In Advances in Neural Information Processing Systems 23, 2010, 9p. [pdf]
E. V. Bonilla, F. V. Agakov, C. K. I. Williams.
Kernel Multi-task Learning using Task-specific Features.
In AISTATS 2007, The Society for Artificial Intelligence and Statistics, 8p. [pdf]
J. Cavazos, C. Dubach, F. V. Agakov, E. V. Bonilla, M. F. P. O'Boyle, G. Fursin, O. Temam.
Automatic Performance Model Construction for the Fast Software Exploration of New Hardware Designs.
In Proc. Int. Conf. on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), ACM, 2006, 11p. [pdf]
E. V. Bonilla, C. K. I. Williams, F. V. Agakov, J. Cavazos, J. Thomson, M. F. P. O'Boyle.
Predictive Search Distributions.
In Proc. ICML, OmniPress, 2006, 8p. [pdf]
F. V. Agakov, E. V. Bonilla, J. Cavazos, B. Franke, G. Fusin, M. F. P. O'Boyle, J. Thomson, M. Toussaint, C. K. I. Williams.
Using Machine Learning to Focus Iterative Optimization.
In the 4th Annual International Symposium on Code Generation and Optimization (CGO), IEEE Comp. Soc., 2006, 11p. [pdf]
F. V. Agakov and D. Barber. Auxiliary Variational Information Maximization for Dimensionality Reduction.
In C. Saunders, M. Grobelnik, S. Gunn, J. Shawe-Taylor (Eds.),
Revised selected papers of Subspace, Latent Structure, and Feature Selection Workshop (SLSFS), LNCS 3940, Springer, 2006, 12p. [ps.gz, pdf]
F.V. Agakov and D. Barber. Kernelized Infomax Clustering.
In Advances in Neural Information Processing Systems 18, MIT Press, 2006, 8p. [ps.gz, pdf]
F. V. Agakov and D. Barber. Variational Information Maximization for Neural Coding.
In N. R. Pal, N. Kasabov, R. K. Mudi et. al. (Eds.), LNCS 3316, Springer, 2004, 6p. [pdf]
F. V. Agakov and D. Barber. An Auxiliary Variational Method.
In N. R. Pal, N. Kasabov, R. K. Mudi et. al. (Eds.), LNCS 3316, Springer, 2004, 6p. [pdf]
D. Barber and F. V. Agakov. The IM Algorithm: A variational approach to Information Maximization.
In Advances in Neural Information Processing Systems 16, MIT Press, 2004, 8p. [ps.gz, pdf]
M. Welling, F. V. Agakov and C. K. I. Williams. Extreme Components Analysis.
In Advances in Neural Information Processing Systems 16, MIT Press, 2004, 8p. [ps.gz, pdf]
F. V. Agakov and D. Barber. Approximate Learning in Temporal Hidden Hopfield Models.
In International Conference on Artificial Neural Networks, Springer-Verlag, 2003, 8p. [ps.gz, pdf].
C. K. I. Williams and F. V. Agakov. Products of Gaussians and Probabilistic Minor Component Analysis.
Neural Computation, Vol. 14, No. 5, MIT Press, 2002. [ps.gz]
C. K. I. Williams, F. V. Agakov and S. N. Felderhof. Products of Gaussians.
In Advances in Neural Information Processing Systems 14, MIT Press, 2002, 8p. [ps.gz]
F. V. Agakov and D. Barber. Variational Information Maximization in Gaussian Channels.
UoE, IANC, Technical Report, EDI-INF-RR-0206, Apr. 2004, 12p. [ps.gz, pdf]
F. V. Agakov and D. Barber. An Auxiliary Variational Method.
UoE, IANC. Technical Report, EDI-INF-RR-0205, Apr 2004, 10p. [ps.gz, pdf]
F. V. Agakov and D. Barber. Temporal Hidden Hopfield Models.
UoE, IANC. Technical Report, EDI-INF-RR-0156, Nov 2002, 18p. [ps.gz, pdf]
(Here are the slides for the related talk I gave at the Probabilistic Brain symposium in Cambridge, UK '03).
D. Barber and F. V. Agakov. Correlated sequence learning in a network
of spiking neurons using maximum likelihood.
UoE, IANC. Technical Report, EDI-INF-RR-0149, Apr 2002, 13p. [ps.gz]
C. K. I. Williams and F. V. Agakov. An Analysis of Contrastive Divergence
Learning in Gaussian Boltzmann Machines.
UoE, IANC. Technical Report, EDI-INF-RR-0120, May 2002, 14p. [ps.gz]
C. K. I. Williams and F. V. Agakov. Products of Gaussians and Probabilistic Minor Component Analysis.
UoE, Institute of Adaptive and Neural Computation. Technical Report, EDI-INF-RR-0043, July 2001, 11p.
[ps.gz]
F. V. Agakov, P. McKeigue, J. Krohn, J. Flint. Sparse Bayesian Instrumental Variable Analysis.
In European Mathematical Genetics Meeting (EMGM), 2010.
F. V. Agakov and D. Barber. Auxiliary Variational Information Maximization for Dimensionality Reduction.
In PASCAL: Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimisation Perspectives Workshop, 2005. [ps.gz, pdf]
F. V. Agakov. Variational Information Maximization in Stochastic Environments.
PhD Thesis, School of Informatics, The University of Edinburgh, 2005, 205p. [pdf]
F. V. Agakov, Investigations of Gaussian Products-of-Experts Models.
Master's Thesis, Division of Informatics, The University of Edinburgh, 2000, 169p. [ps.gz]
Here is the list of older publications.
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Page last updated: Nov. 13, 2011