John D. Lafferty

ORCID

Publication Activity (10 Years)

Years Active: 1990-2019
Publications (10 Years): 12

Top Venues

Publications

Michihiro Yasunaga, John D. Lafferty
TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts. AAAI (2019)
Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John D. Lafferty
Prediction Rule Reshaping. CoRR (2018)
Nikita Mishra, Connor Imes, John D. Lafferty, Henry Hoffmann
CALOREE: Learning Control for Predictable Latency and Low Energy. ASPLOS (2018)
Sabyasachi Chatterjee, John D. Lafferty
Denoising Flows on Trees. IEEE Trans. Inf. Theory 64 (3) (2018)
Yuancheng Zhu, John D. Lafferty
Distributed Nonparametric Regression under Communication Constraints. CoRR (2018)
Adam L. Berger, John D. Lafferty
Information Retrieval as Statistical Translation. SIGIR Forum 51 (2) (2017)
Nikita Mishra, John D. Lafferty, Henry Hoffmann
ESP: A Machine Learning Approach to Predicting Application Interference. ICAC (2017)
Chengxiang Zhai, John D. Lafferty
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. SIGIR Forum 51 (2) (2017)
John D. Lafferty, Chengxiang Zhai
Document Language Models, Query Models, and Risk Minimization for Information Retrieval. SIGIR Forum 51 (2) (2017)
Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu
Local Minimax Complexity of Stochastic Convex Optimization. NIPS (2016)
Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty
Selective inference for group-sparse linear models. NIPS (2016)
Qinqing Zheng, John D. Lafferty
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent. CoRR (2016)
ChengXiang Zhai, William W. Cohen, John D. Lafferty
Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. SIGIR Forum 49 (1) (2015)
Qinqing Zheng, John D. Lafferty
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements. NIPS (2015)
Qinqing Zheng, John D. Lafferty
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements. CoRR (2015)
Nikita Mishra, Huazhe Zhang, John D. Lafferty, Henry Hoffmann
A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints. ASPLOS (2015)
Zhe Liu, John D. Lafferty
Blossom Tree Graphical Models. NIPS (2014)
Yuancheng Zhu, John D. Lafferty
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls. NIPS (2014)
Minhua Chen, John D. Lafferty
Mismatched estimation and relative entropy in vector Gaussian channels. ISIT (2013)
Thomas P. Minka, John D. Lafferty
Expectation-Propogation for the Generative Aspect Model CoRR (2013)
John D. Lafferty, Larry A. Wasserman
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk CoRR (2013)
Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou
The Bigraphical Lasso. ICML (3) (2013)
Dinah Shender, John D. Lafferty
Computation-Risk Tradeoffs for Covariance-Thresholded Regression. ICML (3) (2013)
Han Liu, John D. Lafferty, Larry A. Wasserman
Exponential Concentration for Mutual Information Estimation with Application to Forests. NIPS (2012)
Haijie Gu, John D. Lafferty
Sequential Nonparametric Regression. CoRR (2012)
Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman
High Dimensional Semiparametric Gaussian Copula Graphical Models. ICML (2012)
Pradeep Ravikumar, John D. Lafferty
Variational Chernoff Bounds for Graphical Models CoRR (2012)
Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty
Sparse Additive Functional and Kernel CCA. ICML (2012)
Tuo Zhao, Han Liu, Kathryn Roeder, John D. Lafferty, Larry A. Wasserman
The huge Package for High-dimensional Undirected Graph Estimation in R. J. Mach. Learn. Res. 13 (2012)
Min Xu, John D. Lafferty
Conditional Sparse Coding and Grouped Multivariate Regression. ICML (2012)
Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty
Sparse Additive Functional and Kernel CCA CoRR (2012)
John D. Lafferty, Han Liu, Larry A. Wasserman
Sparse Nonparametric Graphical Models CoRR (2012)
Haijie Gu, John D. Lafferty
Sequential Nonparametric Regression. ICML (2012)
Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman
The Nonparanormal SKEPTIC. CoRR (2012)
Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty
Nonparametric Reduced Rank Regression. NIPS (2012)
Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John D. Lafferty, Larry A. Wasserman
Forest Density Estimation. J. Mach. Learn. Res. 12 (2011)
Kai Yu, Yuanqing Lin, John D. Lafferty
Learning image representations from the pixel level via hierarchical sparse coding. CVPR (2011)
Mladen Kolar, John D. Lafferty, Larry A. Wasserman
Union Support Recovery in Multi-task Learning. J. Mach. Learn. Res. 12 (2011)
Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman
Graph-Valued Regression. NIPS (2010)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Time varying undirected graphs. Mach. Learn. 80 (2-3) (2010)
Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu
Forest Density Estimation. COLT (2010)
Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong
Large-scale collaborative prediction using a nonparametric random effects model. ICML (2009)
Han Liu, John D. Lafferty, Larry A. Wasserman
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. J. Mach. Learn. Res. 10 (2009)
Kai Yu, Shenghuo Zhu, John D. Lafferty, Yihong Gong
Fast nonparametric matrix factorization for large-scale collaborative filtering. SIGIR (2009)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Compressed and Privacy-Sensitive Sparse Regression. IEEE Trans. Inf. Theory 55 (2) (2009)
Han Liu, John D. Lafferty, Larry A. Wasserman
Nonparametric regression and classification with joint sparsity constraints. NIPS (2008)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Time Varying Undirected Graphs. COLT (2008)
Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman
SpAM: Sparse Additive Models. NIPS (2007)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Compressed Regression CoRR (2007)
Ramesh Nallapati, William W. Cohen, John D. Lafferty
Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability. ICDM Workshops (2007)
Noah A. Smith, Douglas L. Vail, John D. Lafferty
Computationally Efficient M-Estimation of Log-Linear Structure Models. ACL (2007)
Douglas L. Vail, Manuela M. Veloso, John D. Lafferty
Conditional random fields for activity recognition. AAMAS (2007)
John D. Lafferty, Larry A. Wasserman
Statistical Analysis of Semi-Supervised Regression. NIPS (2007)
Matus Telgarsky, John D. Lafferty
Signal Decomposition using Multiscale Admixture Models. ICASSP (2) (2007)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Compressed Regression. NIPS (2007)
Han Liu, John D. Lafferty, Larry A. Wasserman
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. AISTATS (2007)
Ramesh Nallapati, Susan Ditmore, John D. Lafferty, Kin Ung
Multiscale topic tomography. KDD (2007)
Douglas L. Vail, John D. Lafferty, Manuela M. Veloso
Feature selection in conditional random fields for activity recognition. IROS (2007)
Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty
-Regularized Logistic Regression. NIPS (2006)
David M. Blei, John D. Lafferty
Dynamic topic models. ICML (2006)
ChengXiang Zhai, John D. Lafferty
A risk minimization framework for information retrieval. Inf. Process. Manag. 42 (1) (2006)
Pradeep Ravikumar, John D. Lafferty
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation. ICML (2006)
John D. Lafferty, Larry A. Wasserman
Rodeo: Sparse Nonparametric Regression in High Dimensions. NIPS (2005)
David M. Blei, John D. Lafferty
Correlated Topic Models. NIPS (2005)
John D. Lafferty, Guy Lebanon
Diffusion Kernels on Statistical Manifolds. J. Mach. Learn. Res. 6 (2005)
Xiaojin Zhu, John D. Lafferty
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. ICML (2005)
Pradeep Ravikumar, John D. Lafferty
Preconditioner Approximations for Probabilistic Graphical Models. NIPS (2005)
Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy
Semi-supervised learning using randomized mincuts. ICML (2004)
Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, John D. Lafferty
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning. NIPS (2004)
John D. Lafferty, Xiaojin Zhu, Yan Liu
Kernel conditional random fields: representation and clique selection. ICML (2004)
Guy Lebanon, John D. Lafferty
Hyperplane margin classifiers on the multinomial manifold. ICML (2004)
ChengXiang Zhai, John D. Lafferty
A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22 (2) (2004)
Pradeep Ravikumar, John D. Lafferty
Variational Chernoff Bounds for Graphical Models. UAI (2004)
James Allan, Jay Aslam, Nicholas J. Belkin, Chris Buckley, James P. Callan, W. Bruce Croft, Susan T. Dumais, Norbert Fuhr, Donna Harman, David J. Harper, Djoerd Hiemstra, Thomas Hofmann, Eduard H. Hovy, Wessel Kraaij, John D. Lafferty, Victor Lavrenko, David D. Lewis, Liz Liddy, R. Manmatha, Andrew McCallum, Jay M. Ponte, John M. Prager, Dragomir R. Radev, Philip Resnik, Stephen E. Robertson, Ronald Rosenfeld, Salim Roukos, Mark Sanderson, Richard M. Schwartz, Amit Singhal, Alan F. Smeaton, Howard R. Turtle, Ellen M. Voorhees, Ralph M. Weischedel, Jinxi Xu, ChengXiang Zhai
Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. SIGIR Forum 37 (1) (2003)
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML (2003)
ChengXiang Zhai, William W. Cohen, John D. Lafferty
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. SIGIR (2003)
Shyjan Mahamud, Martial Hebert, John D. Lafferty
Combining Simple Discriminators for Object Discrimination. ECCV (3) (2002)
John D. Lafferty, Guy Lebanon
Information Diffusion Kernels. NIPS (2002)
Risi Kondor, John D. Lafferty
Diffusion Kernels on Graphs and Other Discrete Input Spaces. ICML (2002)
Thomas P. Minka, John D. Lafferty
Expectation-Propogation for the Generative Aspect Model. UAI (2002)
Guy Lebanon, John D. Lafferty
Cranking: Combining Rankings Using Conditional Probability Models on Permutations. ICML (2002)
Guy Lebanon, John D. Lafferty
Conditional Models on the Ranking Poset. NIPS (2002)
ChengXiang Zhai, John D. Lafferty
Two-stage language models for information retrieval. SIGIR (2002)
John D. Lafferty, ChengXiang Zhai
Document Language Models, Query Models, and Risk Minimization for Information Retrieval. SIGIR (2001)
ChengXiang Zhai, John D. Lafferty
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. SIGIR (2001)
John D. Lafferty, Larry A. Wasserman
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk. UAI (2001)
John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML (2001)
Guy Lebanon, John D. Lafferty
Boosting and Maximum Likelihood for Exponential Models. NIPS (2001)
W. Bruce Croft, James P. Callan, John D. Lafferty
Workshop on language modeling and information retrieval. SIGIR Forum 35 (1) (2001)
ChengXiang Zhai, John D. Lafferty
Model-based Feedback in the Language Modeling Approach to Information Retrieval. CIKM (2001)
John D. Lafferty
Additive Models, Boosting, and Inference for Generalized Divergences. COLT (1999)
John D. Lafferty, Alexander Vardy
Ordered Binary Decision Diagrams and Minimal Trellises. IEEE Trans. Computers 48 (9) (1999)
Adam L. Berger, John D. Lafferty
Information Retrieval as Statistical Translation. SIGIR (1999)
Doug Beeferman, Adam L. Berger, John D. Lafferty
Statistical Models for Text Segmentation. Mach. Learn. 34 (1-3) (1999)
Adam L. Berger, John D. Lafferty
The Weaver System for Document Retrieval. TREC (1999)
Doug Beeferman, Adam L. Berger, John D. Lafferty
Cyberpunc: a lightweight punctuation annotation system for speech. ICASSP (1998)
Stephen Della Pietra, Vincent J. Della Pietra, John D. Lafferty
Inducing Features of Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 19 (4) (1997)
Doug Beeferman, Adam L. Berger, John D. Lafferty
Text Segmentation Using Exponential Models. EMNLP (1997)
Doug Beeferman, Adam L. Berger, John D. Lafferty
A Model of Lexical Attraction and Repulsion CoRR (1997)
John D. Lafferty, Daniel N. Rockmore
Spectral Techniques for Expander Codes. STOC (1997)

John D. Lafferty.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

John D. Lafferty