Kernel Methods

Kernel Methods

  • Revisiting k-means: New algorithms via Bayesian nonparametrics. B. Kulis and M. I. Jordan. In J. Langford and J. Pineau (Eds.), Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, UK, 2012.

  • Bayesian generalized kernel mixed models. Z. Zhang, G. Dai, and M. I. Jordan. Journal of Machine Learning Research, 12, 111-139, 2011.

  • Unsupervised kernel dimension reduction. M. Wang, F. Sha, and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.) Advances in Neural Information Processing Systems (NIPS) 23, to appear.

  • Regularized discriminant analysis, ridge regression and beyond. Z. Zhang, G. Dai, C. Xu, and M. I. Jordan. Journal of Machine Learning Research, 11, 2141-2170, 2010.

  • Posterior consistency of the Silverman g-prior in Bayesian model choice. Z. Zhang and M. I. Jordan. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009.

  • Kernel dimension reduction in regression. K. Fukumizu, F. R. Bach, and M. I. Jordan. Annals of Statistics, 37, 1871-1905, 2009.

  • Regression on manifolds using kernel dimension reduction. J. Nilsson, F. Sha, and M. I. Jordan. Proceedings of the 24th International Conference on Machine Learning (ICML), 2007.

  • Learning spectral clustering, with application to speech separation. F. R. Bach, and M. I. Jordan. Journal of Machine Learning Research, 7, 1963-2001, 2006.

  • Comment on 'Support vector machines with applications'. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. Statistical Science, 21, 341-346, 2006.

  • Predictive low-rank decomposition for kernel methods. F. R. Bach and M. I. Jordan. Proceedings of the 22nd International Conference on Machine Learning (ICML), 2005. [Matlab code]

  • A kernel-based learning approach to ad hoc sensor network localization. X. Nguyen, M. I. Jordan, and B. Sinopoli. ACM Transactions on Sensor Networks, 1, 134-152, 2005.

  • Semiparametric latent factor models. Y. W. Teh, M. Seeger, and M. I. Jordan. Proceedings of the Conference on Artificial Intelligence and Statistics (AISTATS), 2005.

  • Nonparametric decentralized detection using kernel methods. X. Nguyen, M. J. Wainwright, and M. I. Jordan. IEEE Transactions on Signal Processing, 53, 4053-4066, 2005.

  • Computing regularization paths for learning multiple kernels. F. R. Bach, R. Thibaux, and M. I. Jordan. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005. [Matlab code]

  • Multiple kernel learning, conic duality, and the SMO algorithm. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004. [Long version]. [Software].

  • Decentralized detection and classification using kernel methods. X. Nguyen, M. J. Wainwright, and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004.

  • Learning graphical models for stationary time series. F. R. Bach and M. I. Jordan. IEEE Transactions on Signal Processing, 52, 2189-2199, 2004.

  • A statistical framework for genomic data fusion. G. R. G. Lanckriet, T. De Bie, N. Cristianini, M. I. Jordan, and W. S. Noble. Bioinformatics, 20, 2626-2635, 2004.

  • Sparse Gaussian process classification with multiple classes. M. Seeger and M. I. Jordan. Technical Report 661, Department of Statistics, University of California, Berkeley, 2004.

  • Kernel-based data fusion and its application to protein function prediction in yeast. G. R. G. Lanckriet, M. Deng, N. Cristianini, M. I. Jordan, and W. S. Noble. Pacific Symposium on Biocomputing (PSB), 2004. [Supplementary information].

  • 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.

  • Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces. K. Fukumizu, F. R. Bach, and M. I. Jordan. Journal of Machine Learning Research, 5, 73-79, 2004.

  • Learning spectral clustering. F. R. Bach and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, (long version), 2004.

  • Kernel dimensionality reduction for supervised learning. K. Fukumizu, F. R. Bach, and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.

  • Kernel-based integration of genomic data using semidefinite programming. G. R. G. Lanckriet, N. Cristianini, M. I. Jordan, and W. S. Noble. In B. Schoelkopf, K. Tsuda and J-P. Vert (Eds.), Kernel Methods in Computational Biology, Cambridge, MA: MIT Press, 2003.

  • Support vector machines for analog circuit performance representation. F. De Bernardinis, M. I. Jordan, and A. L. Sangiovanni-Vincentelli. Proceedings of the Design Automation Conference (DAC), 2003.

  • Robust novelty detection with single-class MPM. G. R. G. Lanckriet, L. El Ghaoui, and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.

  • Kernel independent component analysis. F. R. Bach and M. I. Jordan. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003, [Long version]. [Matlab code]

  • Learning graphical models with Mercer kernels. F. R. Bach and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.

  • Kernel independent component analysis. F. R. Bach and M. I. Jordan. Journal of Machine Learning Research, 3, 1-48, 2002. [Matlab code]

  • A robust minimax approach to classification. G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan. Journal of Machine Learning Research, 3, 552-582, 2002. [Matlab code]

  • Learning the kernel matrix with semidefinite programming. G. R. G. Lanckriet, P. L. Bartlett, N. Cristianini, L. El Ghaoui, and M. I. Jordan. Machine Learning: Proceedings of the Nineteenth International Conference (ICML), San Mateo, CA: Morgan Kaufmann, 2002.

  • Minimax probability machine. G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002.