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All publications by John D. Lafferty
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Exponential Concentration for Mutual Information Estimation with Application to Forests
Han Liu, Larry Wasserman and John D. Lafferty
Advances in Neural Information Processing Systems 25, 2012


Nonparametric Reduced Rank Regression
Rina Foygel, Michael Horrell, Mathias Drton and John D. Lafferty
Advances in Neural Information Processing Systems 25, 2012


Conditional Sparse Coding and Grouped Multivariate Regression
Min Xu and John D. Lafferty
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


The Nonparanormal SKEPTIC
Han Liu, Fang Han, Ming Yuan, Larry Wasserman and John D. Lafferty
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Sequential Nonparametric Regression
Haijie Gu and John D. Lafferty
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Sparse Additive Functional and Kernel CCA
Sivaraman Balakrishnan, Kriti Puniyani and John D. Lafferty
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Graph-Valued Regression
Han Liu, Xi Chen, Larry Wasserman and John D. Lafferty
Advances in Neural Information Processing Systems 23, 2010


The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Han Liu, John D. Lafferty and Larry A. Wasserman
Journal of Machine Learning Research, 2009


Large-scale collaborative prediction using a nonparametric random effects model
Kai Yu, John D. Lafferty, Shenghuo Zhu and Yihong Gong
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Nonparametric regression and classification with joint sparsity constraints
Han Liu, Larry Wasserman and John D. Lafferty
Advances in Neural Information Processing Systems 21, 2008


SpAM: Sparse Additive Models
Han Liu, Larry Wasserman, John D. Lafferty and Pradeep K. Ravikumar
Advances in Neural Information Processing Systems 20, 2007


Statistical Analysis of Semi-Supervised Regression
Larry Wasserman and John D. Lafferty
Advances in Neural Information Processing Systems 20, 2007


Compressed Regression
Shuheng Zhou, Larry Wasserman and John D. Lafferty
Advances in Neural Information Processing Systems 20, 2007


Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
Han Liu, John D. Lafferty and Larry A. Wasserman
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Quadratic programming relaxations for metric labeling and Markov random field MAP estimation
Pradeep D. Ravikumar and John D. Lafferty
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Dynamic topic models
David M. Blei and John D. Lafferty
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression
Martin J. Wainwright, John D. Lafferty and Pradeep K. Ravikumar
Advances in Neural Information Processing Systems 19, 2006


Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning
Xiaojin Zhu and John D. Lafferty
Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005


Correlated Topic Models
John D. Lafferty and David M. Blei
Advances in Neural Information Processing Systems 18, 2005


Preconditioner Approximations for Probabilistic Graphical Models
John D. Lafferty and Pradeep K. Ravikumar
Advances in Neural Information Processing Systems 18, 2005


Rodeo: Sparse Nonparametric Regression in High Dimensions
Larry Wasserman and John D. Lafferty
Advances in Neural Information Processing Systems 18, 2005


Diffusion Kernels on Statistical Manifolds
John D. Lafferty and Guy Lebanon
Journal of Machine Learning Research, 2005


Hyperplane margin classifiers on the multinomial manifold
Guy Lebanon and John D. Lafferty
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Kernel conditional random fields: representation and clique selection
John D. Lafferty, Xiaojin Zhu and Yan Liu
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Semi-supervised learning using randomized mincuts
Avrim Blum, John D. Lafferty, Mugizi R. Rwebangira and Rajashekar Reddy
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning
Xiaojin Zhu, Jaz Kandola, Zoubin Ghahramani and John D. Lafferty
Advances in Neural Information Processing Systems 17, 2004


Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
Xiaojin Zhu, Zoubin Ghahramani and John D. Lafferty
Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003


Conditional Models on the Ranking Poset
Guy Lebanon and John D. Lafferty
Advances in Neural Information Processing Systems 15, 2002


Information Diffusion Kernels
Guy Lebanon and John D. Lafferty
Advances in Neural Information Processing Systems 15, 2002


Boosting and Maximum Likelihood for Exponential Models
Guy Lebanon and John D. Lafferty
Advances in Neural Information Processing Systems 14, 2001