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I also maintain a Google scholar page.
Journal Papers
Tipping, M. E. and N. D. Lawrence (2005). Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. NeuroComputing 69, 123–141. [Abstract]
Weston, J., A. Elisseeff, B. Schölkopf, and M. E. Tipping (2003). Use of the zero-norm with linear models and kernel methods. Journal of Machine Learning Research 3, 1439–1461. [Abstract] [Available from JMLR]
Li, Y., C. Campbell, and M. E. Tipping (2002). Bayesian automatic relevance determination algorithms for classifying gene expression data. Bioinformatics 18(10), 1332–1339. [Abstract]
Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211–244. [Abstract] [Available from JMLR]
Tipping, M. E. and C. M. Bishop (1999a). Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61(3), 611–622. [Abstract] [Request a copy]
Tipping, M. E. and C. M. Bishop (1999b). Mixtures of probabilistic principal component analysers. Neural Computation 11(2), 443–482. [Abstract] [PDF] [gzipped PostScript]
Bishop, C. M. and M. E. Tipping (1998). A hierarchical latent variable model for data visualization. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 281–293. [Abstract] [PDF]
Tipping, M. E. and D. Lowe (1998). Shadow targets: a novel algorithm for topographic projections by radial basis functions. NeuroComputing 19, 211–222. [Abstract]
Lowe, D. and M. E. Tipping (1996). Feed-forward neural networks and topographic mappings for exploratory data analysis. Neural Computing and Applications 4, 83–95. [Abstract] [gzipped PostScript]
Conference Papers
Zaragoza, H., D. Hiemstra, and M. E. Tipping (2003). Bayesian extension to the language model for ad hoc information retrieval. In Proceedings of the 26th International ACM SIGIR Conference, pp. 4–9.
Tipping, M. E. and N. D. Lawrence (2003). A variational approach to robust Bayesian interpolation. In Proceedings of the IEEE International Workshop on Neural Networks for Signal Processing.
Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6. [Abstract] [PDF] [gzipped PostScript]
Tipping, M. E. and C. M. Bishop (2003). Bayesian image super-resolution. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15. MIT Press. [Abstract] [PDF] [gzipped PostScript]
Faul, A. C. and M. E. Tipping (2002). Analysis of sparse Bayesian learning. In T. G. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14, pp. 383–389. MIT Press. [Abstract] [gzipped PostScript]
Faul, A. and M. E. Tipping (2001). A variational approach to robust regression. In G. Dorffner, H. Bischof, and K. Hornik (Eds.), Proceedings of ICANN'01, pp. 95–102. Springer. [Abstract] [gzipped PostScript]
Tipping, M. E. and B. Schölkopf (2001). A kernel approach for vector quantization with guaranteed distortion bounds. In T. Jaakkola and T. Richardson (Eds.), Artificial Intelligence and Statistics 2001, pp. 129–134. Morgan Kaufmann. [Abstract] [gzipped PostScript]
Tipping, M. E. (2001). Sparse kernel principal component analysis. In Advances in Neural Information Processing Systems 13. MIT Press. [Abstract] [gzipped PostScript]
Bishop, C. M. and M. E. Tipping (2000). Variational relevance vector machines. In C. Boutilier and M. Goldszmidt (Eds.), Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 46–53. Morgan Kaufmann. [Abstract] [PDF] [gzipped PostScript]
Tipping, M. E. (2000). The Relevance Vector Machine. In S. A. Solla, T. K. Leen, and K.-R. Müller (Eds.), Advances in Neural Information Processing Systems 12, pp. 652–658. MIT Press. [Abstract] [gzipped PostScript]
Tipping, M. E. (1999a). Deriving cluster analytic distance functions from Gaussian mixture models. In Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN99), Volume 2, pp. 815–820. IEE. [Abstract] [gzipped PostScript]
Tipping, M. E. (1999b). Probabilistic visualisation of high-dimensional binary data. In M. S. Kearns, S. A. Solla, and D. A. Cohn (Eds.), Advances in Neural Information Processing Systems 11, Cambridge, MA, pp. 592–598. MIT Press. [Abstract] [gzipped PostScript]
Tipping, M. E. and C. M. Bishop (1997). Mixtures of principal component analysers. In Proceedings of the IEE Fifth International Conference on Artificial Neural Networks, Cambridge, pp. 13–18. London: IEE. [Abstract] [gzipped PostScript]
Tipping, M. E. and D. Lowe (1997). Shadow targets: a novel algorithm for topographic projections by radial basis functions. In IEE Fifth International Conference on Artificial Neural Networks, London, pp. 101–105. IEE. [Abstract] [gzipped PostScript]
Tipping, M. E. and C. M. Bishop (1997). Hierarchical models for data visualization. In Proceedings of the IEE Fifth International Conference on Artificial Neural Networks, Cambridge, pp. 70–75. London: IEE.
Lowe, D. and M. E. Tipping (1997). Neuroscale: Novel topographic feature extraction with radial basis function networks. In M. Mozer, M. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems 9, pp. 543–549. Cambridge, Mass: MIT Press. [Abstract] [gzipped PostScript]
Lowe, D. and M. E. Tipping (1995). A novel neural network technique for exploratory data analysis. In Proceedings of ICANN '95 (Scientific Conference), Volume 1, pp. 339–344. Paris: EC2 & Cie. [Abstract] [gzipped PostScript]
Book Chapters
Tipping, M. E. (2004). Bayesian inference: An introduction to principles and practice in machine learning. In O. Bousquet, U. von Luxburg, and G. Rätsch (Eds.), Advanced Lectures on Machine Learning, pp. 41–62. Springer. [Abstract] [PDF] [gzipped PostScript]
Bishop, C. M. and M. E. Tipping (2003). Bayesian regression and classification. In J. Suykens, G. Horvath, S. Basu, C. Micchelli, and J. Vandewalle (Eds.), Advances in Learning Theory: Methods, Models and Applications, Volume 190 of NATO Science Series III: Computer & Systems Sciences. IOS Press, Amsterdam.
Bishop, C. M. and M. E. Tipping (2000). Variational relevance vector machines. In V. Nunez-Anton and E. Ferreira (Eds.), Proceedings of the 15th International Workshop on Statistical Modelling, Bilbao, Spain, pp. 1–17.
Ph.D. Thesis
Tipping, M. E. (1996). Topographic Mappings and Feed-Forward Neural Networks. Ph. D. thesis, Aston University, Aston Street, Birmingham B4 7ET, UK. [More information and download]