Lafferty, John D.

42 publications

NeurIPS 2021 Convergence and Alignment of Gradient Descent with Random Backpropagation Weights Ganlin Song, Ruitu Xu, John D. Lafferty

AAAI 2019 TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts Michihiro Yasunaga, John D. Lafferty

ICML 2012 Conditional Sparse Coding and Grouped Multivariate Regression Min Xu, John D. Lafferty

NeurIPS 2012 Exponential Concentration for Mutual Information Estimation with Application to Forests Han Liu, Larry Wasserman, John D. Lafferty

ICML 2012 High Dimensional Semiparametric Gaussian Copula Graphical Models Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman

NeurIPS 2012 Nonparametric Reduced Rank Regression Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty

ICML 2012 Sequential Nonparametric Regression Haijie Gu, John D. Lafferty

ICML 2012 Sparse Additive Functional and Kernel CCA Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty

CVPR 2011 Learning Image Representations from the Pixel Level via Hierarchical Sparse Coding Kai Yu, Yuanqing Lin, John D. Lafferty

COLT 2010 Forest Density Estimation Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu

NeurIPS 2010 Graph-Valued Regression Han Liu, Xi Chen, Larry Wasserman, John D. Lafferty

MLJ 2010 Time Varying Undirected Graphs Shuheng Zhou, John D. Lafferty, Larry A. Wasserman

ICML 2009 Large-Scale Collaborative Prediction Using a Nonparametric Random Effects Model Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong

NeurIPS 2008 Nonparametric Regression and Classification with Joint Sparsity Constraints Han Liu, Larry Wasserman, John D. Lafferty

COLT 2008 Time Varying Undirected Graphs Shuheng Zhou, John D. Lafferty, Larry A. Wasserman

NeurIPS 2007 Compressed Regression Shuheng Zhou, Larry Wasserman, John D. Lafferty

NeurIPS 2007 SpAM: Sparse Additive Models Han Liu, Larry Wasserman, John D. Lafferty, Pradeep K. Ravikumar

NeurIPS 2007 Statistical Analysis of Semi-Supervised Regression Larry Wasserman, John D. Lafferty

ICML 2006 Dynamic Topic Models David M. Blei, John D. Lafferty

NeurIPS 2006 High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression Martin J. Wainwright, John D. Lafferty, Pradeep K. Ravikumar

ICML 2006 Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation Pradeep Ravikumar, John D. Lafferty

NeurIPS 2005 Correlated Topic Models John D. Lafferty, David M. Blei

ICML 2005 Harmonic Mixtures: Combining Mixture Models and Graph-Based Methods for Inductive and Scalable Semi-Supervised Learning Xiaojin Zhu, John D. Lafferty

NeurIPS 2005 Preconditioner Approximations for Probabilistic Graphical Models John D. Lafferty, Pradeep K. Ravikumar

NeurIPS 2005 Rodeo: Sparse Nonparametric Regression in High Dimensions Larry Wasserman, John D. Lafferty

ICML 2004 Hyperplane Margin Classifiers on the Multinomial Manifold Guy Lebanon, John D. Lafferty

ICML 2004 Kernel Conditional Random Fields: Representation and Clique Selection John D. Lafferty, Xiaojin Zhu, Yan Liu

NeurIPS 2004 Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning Xiaojin Zhu, Jaz Kandola, Zoubin Ghahramani, John D. Lafferty

ICML 2004 Semi-Supervised Learning Using Randomized Mincuts Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy

UAI 2004 Variational Chernoff Bounds for Graphical Models Pradeep Ravikumar, John D. Lafferty

ICML 2003 Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty

ECCV 2002 Combining Simple Discriminators for Object Discrimination Shyjan Mahamud, Martial Hebert, John D. Lafferty

NeurIPS 2002 Conditional Models on the Ranking Poset Guy Lebanon, John D. Lafferty

ICML 2002 Cranking: Combining Rankings Using Conditional Probability Models on Permutations Guy Lebanon, John D. Lafferty

ICML 2002 Diffusion Kernels on Graphs and Other Discrete Input Spaces Risi Kondor, John D. Lafferty

UAI 2002 Expectation-Propogation for the Generative Aspect Model Thomas P. Minka, John D. Lafferty

NeurIPS 2002 Information Diffusion Kernels Guy Lebanon, John D. Lafferty

NeurIPS 2001 Boosting and Maximum Likelihood for Exponential Models Guy Lebanon, John D. Lafferty

ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira

UAI 2001 Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk John D. Lafferty, Larry A. Wasserman

COLT 1999 Additive Models, Boosting, and Inference for Generalized Divergences John D. Lafferty

MLJ 1999 Statistical Models for Text Segmentation Doug Beeferman, Adam L. Berger, John D. Lafferty