Readings

• An introduction to kernel-based learning algorithms. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. IEEE Neural Networks, 12(2):181-201, 2001.

• Nonlinear component analysis as a kernel eigenvalue problem. B. Schölkopf, A. Smola, and K.-R. Müller. Neural Computation, 10:1299-1319, 1998.

• Kernel independent component analysis. F. R. Bach and M. I. Jordan. Journal of Machine Learning Research, 3, 1-48, 2002. [Read sections 2.1 and 3.2 for now].

• Convolution kernels on discrete structures. D. Haussler. Technical Report UCSC-CRL-99-10, University of California, Santa Cruz.

• Positive definite rational kernels. C. Cortes, P. Haffner, and M. Mohri. Proceedings of the Conference on Computational Learning Theory, 2003.

• Marginalized kernels for biological sequences. K. Tsuda, T. Kin and K. Asai. Bioinformatics, 18(Suppl 1), 268-275, 2002.

• Learning the kernel matrix with semidefinite programming. G. R. G. Lanckriet, N. Cristianini, L. El Ghaoui, P. L. Bartlett, and M. I. Jordan. Journal of Machine Learning Research, 5:27-72, 2004.

• Prediction with Gaussian processes: from linear regression to linear prediction and beyond. C. Williams. In ``Learning and Inference in Graphical Models,'' MIT Press, 1999.

• On spectral clustering: Analysis and an algorithm. A. Ng, M. I. Jordan, and Y. Weiss. In Advances in Neural Information Processing (NIPS) 14, MIT Press, 2002.

• Multi-class spectral graph partitioning. S. X. Yu and J. Shi. In International Conference on Computer Vision, 2003.

• Learning spectral clustering. F. Bach and M. I. Jordan. In Advances in Neural Information Processing (NIPS) 16, MIT Press, 2004.

• Cluster kernels for semi-supervised learning. O. Chapelle, J. Weston and B. Schoelkopf. In Advances in Neural Information Processing (NIPS) 14, MIT Press, 2002.

• An introduction to MCMC for machine learning. C. Andrieu, N. de Freitas, A. Doucet and M. I. Jordan. Machine Learning, 50, 5-43, 2003.

• A Bayesian analysis of some nonparametric problems. T. S. Ferguson. Annals of Statistics, 1, 209-230, 1973.

• Ferguson distributions via Polya urn schemes. D. Blackwell and J. MacQueen. Annals of Statistics, 1, 353-355, 1973.

• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. C. Antoniak. Annals of Statistics, 2, 1152-1174, 1974.

• Bayesian density estimation and inference using mixtures. M. Escobar and M. West. Journal of the American Statistical Association, 90, 577-588, 1995.

• A constructive definition of Dirichlet priors. J. Sethuraman. Statistica Sinica, 4, 639-650, 1994.

• Markov chain sampling methods for Dirichlet processes mixture models. R. Neal. Technical Report 9815, Department of Statistics, University of Toronto, 1998.

• Bayesian haplotype inference via the Dirichlet process. E. P. Xing, R. Sharan and M. I. Jordan. Technical Report CSD-03-1275, Division of Computer Science, University of California, Berkeley, 2003.

• Hierarchical topic models and the nested Chinese restaurant process. D. M. Blei, T. Griffiths, M. I. Jordan, and J. Tenenbaum. In press: Advances in Neural Information Processing Systems (NIPS) 16, 2003.

• Hierarchical Dirichlet processes. Y. W. Teh, M. I. Jordan, M. J. Beal and D. M. Blei. Technical Report 653, Department of Statistics, University of California, Berkeley, 2004.

• Convexity, classification, and risk bounds. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. Technical Report 638, Department of Statistics, University of California, Berkeley, 2003.

• Concentration-of-measure inequalities. G. Lugosi. Department of Economics, Pompeu Fabra University, 2004.

• A few notes on Statistical Learning Theory S. Mendelson. In Advanced Lectures in Machine Learning, LNCS 2600, New York: Springer, 2003.