# Gaussian Process Pseudo-Likelihood Models for Sequence Labeling

- Published: 25 Dec 2014

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Y. Altun, T. Hofmann, and A. J. Smola. Gaussian Process Classification for Segmenting and Annotating Sequences. In ICML, 2004. [OpenAIRE]

P. Balamurugan, S. Shevade, S. Sundararajan, and S.S. Keerthi. A Sequential Dual Method for Structural SVMs. In SDM, pages 223-234, 2011.

D. P. Bertsekas. Nonlinear Programming. Athena Scientific, 1999.

J. Besag. Statistical analysis of non-lattice data. The Statistician, 24:179-195, 1975.

L. Bottou. Large-Scale Machine Learning with Stochastic Gradient Descent. In COMPSTAT, 2010.

S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.

S. Bratieres, N. Quadrianto, and Z. Ghahramani. Bayesian Structured Prediction Using Gaussian Processes. IEEE transactions on Pattern Analysis and Machine Intelligence, 2014a. [OpenAIRE]

S. Bratieres, N. Quadrianto, S. Nowozin, and Z. Ghahramani. Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications. ICML, 2014b. [OpenAIRE]

K. M. A. Chai. Variational Multinomial Logit Gaussian Process. J. Mach. Learn. Res., 13, 2012.

M. Girolami and S. Rogers. Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. Neural Computation, 18(8):1790-1817, 2006. [OpenAIRE]

D. Heckerman, D. M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. J. Mach. Learn. Res., 1:49-75, 2001.

M. E. Khan, S. Mohamed, and K. P. Murphy. Fast Bayesian Inference for NonConjugate Gaussian Process Regression. In NIPS, pages 3149-3157, 2012.

J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In ICML, pages 282- 289, 2001.

J. D. Lafferty, X. Zhu, and Y Liu. Kernel Conditional Random Fields: Representation and Clique Selection. In ICML, volume 69, 2004.

Q Li, J Wang, D. P. Wipf, and Z. Tu. Fixed-Point Model For Structured Labeling. In ICML, pages 214-221, 2013.

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##### Related research

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Y. Altun, T. Hofmann, and A. J. Smola. Gaussian Process Classification for Segmenting and Annotating Sequences. In ICML, 2004. [OpenAIRE]

P. Balamurugan, S. Shevade, S. Sundararajan, and S.S. Keerthi. A Sequential Dual Method for Structural SVMs. In SDM, pages 223-234, 2011.

D. P. Bertsekas. Nonlinear Programming. Athena Scientific, 1999.

J. Besag. Statistical analysis of non-lattice data. The Statistician, 24:179-195, 1975.

L. Bottou. Large-Scale Machine Learning with Stochastic Gradient Descent. In COMPSTAT, 2010.

S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.

S. Bratieres, N. Quadrianto, and Z. Ghahramani. Bayesian Structured Prediction Using Gaussian Processes. IEEE transactions on Pattern Analysis and Machine Intelligence, 2014a. [OpenAIRE]

S. Bratieres, N. Quadrianto, S. Nowozin, and Z. Ghahramani. Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications. ICML, 2014b. [OpenAIRE]

K. M. A. Chai. Variational Multinomial Logit Gaussian Process. J. Mach. Learn. Res., 13, 2012.

M. Girolami and S. Rogers. Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. Neural Computation, 18(8):1790-1817, 2006. [OpenAIRE]

D. Heckerman, D. M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. J. Mach. Learn. Res., 1:49-75, 2001.

M. E. Khan, S. Mohamed, and K. P. Murphy. Fast Bayesian Inference for NonConjugate Gaussian Process Regression. In NIPS, pages 3149-3157, 2012.

J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In ICML, pages 282- 289, 2001.

J. D. Lafferty, X. Zhu, and Y Liu. Kernel Conditional Random Fields: Representation and Clique Selection. In ICML, volume 69, 2004.

Q Li, J Wang, D. P. Wipf, and Z. Tu. Fixed-Point Model For Structured Labeling. In ICML, pages 214-221, 2013.

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