Timezone: »
Spotlight
Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood
Martin J Wainwright · Pradeep Ravikumar · John Lafferty
Tue Dec 05 05:00 PM -- 05:00 PM (PST) @
Author Information
Martin J Wainwright (UC Berkeley)
Pradeep Ravikumar (Carnegie Mellon University)
John Lafferty (Yale University)
More from the Same Authors
-
2021 Poster: Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning »
Andrea Zanette · Martin J Wainwright · Emma Brunskill -
2017 Poster: Kernel Feature Selection via Conditional Covariance Minimization »
Jianbo Chen · Mitchell Stern · Martin J Wainwright · Michael Jordan -
2016 Workshop: Adaptive and Scalable Nonparametric Methods in Machine Learning »
Aaditya Ramdas · Arthur Gretton · Bharath Sriperumbudur · Han Liu · John Lafferty · Samory Kpotufe · Zoltán Szabó -
2016 Poster: Local Minimax Complexity of Stochastic Convex Optimization »
sabyasachi chatterjee · John Duchi · John Lafferty · Yuancheng Zhu -
2016 Poster: Selective inference for group-sparse linear models »
Fan Yang · Rina Barber · Prateek Jain · John Lafferty -
2016 Poster: Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences »
Chi Jin · Yuchen Zhang · Sivaraman Balakrishnan · Martin J Wainwright · Michael Jordan -
2016 Poster: Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain »
Ian En-Hsu Yen · Xiangru Huang · Kai Zhong · Ruohan Zhang · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Fast Classification Rates for High-dimensional Gaussian Generative Models »
Tianyang Li · Adarsh Prasad · Pradeep Ravikumar -
2015 Poster: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon -
2015 Spotlight: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs »
Vidyashankar Sivakumar · Arindam Banerjee · Pradeep Ravikumar -
2015 Poster: Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent »
Ian En-Hsu Yen · Kai Zhong · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial »
David I Inouye · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Consistent Multilabel Classification »
Oluwasanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements »
Qinqing Zheng · John Lafferty -
2015 Poster: Closed-form Estimators for High-dimensional Generalized Linear Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2015 Spotlight: Closed-form Estimators for High-dimensional Generalized Linear Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2014 Poster: QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models »
Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Stephen Becker · Peder A Olsen -
2014 Poster: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: On the Information Theoretic Limits of Learning Ising Models »
Rashish Tandon · Karthikeyan Shanmugam · Pradeep Ravikumar · Alex Dimakis -
2014 Poster: Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space »
Ian En-Hsu Yen · Ting-Wei Lin · Shou-De Lin · Pradeep Ravikumar · Inderjit Dhillon -
2014 Spotlight: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators »
Kai Zhong · Ian En-Hsu Yen · Inderjit Dhillon · Pradeep Ravikumar -
2014 Poster: A Representation Theory for Ranking Functions »
Harsh H Pareek · Pradeep Ravikumar -
2014 Poster: Blossom Tree Graphical Models »
Zhe Liu · John Lafferty -
2014 Poster: Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs »
David I Inouye · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings »
Ian En-Hsu Yen · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Elementary Estimators for Graphical Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2014 Poster: Quantized Estimation of Gaussian Sequence Models in Euclidean Balls »
Yuancheng Zhu · John Lafferty -
2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Poster: Conditional Random Fields via Univariate Exponential Families »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu -
2013 Poster: On Poisson Graphical Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu -
2013 Poster: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack -
2013 Oral: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack -
2013 Poster: Dirty Statistical Models »
Eunho Yang · Pradeep Ravikumar -
2013 Poster: Large Scale Distributed Sparse Precision Estimation »
Huahua Wang · Arindam Banerjee · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2013 Poster: Learning with Noisy Labels »
Nagarajan Natarajan · Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
Stefanie Jegelka · Andreas Krause · Jeffrey A Bilmes · Pradeep Ravikumar -
2012 Workshop: Modern Nonparametric Methods in Machine Learning »
Sivaraman Balakrishnan · Arthur Gretton · Mladen Kolar · John Lafferty · Han Liu · Tong Zhang -
2012 Poster: Nonparametric Reduced Rank Regression »
Rina Foygel · Michael Horrell · Mathias Drton · John Lafferty -
2012 Poster: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Graphical Models via Generalized Linear Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu -
2012 Poster: Communication-Efficient Algorithms for Statistical Optimization »
Yuchen Zhang · John Duchi · Martin J Wainwright -
2012 Poster: No voodoo here! Learning discrete graphical models via inverse covariance estimation »
Po-Ling Loh · Martin J Wainwright -
2012 Oral: No voodoo here! Learning discrete graphical models via inverse covariance estimation »
Po-Ling Loh · Martin J Wainwright -
2012 Oral: Graphical Models via Generalized Linear Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu -
2012 Oral: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions »
Alekh Agarwal · Sahand N Negahban · Martin J Wainwright -
2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
John Duchi · Michael Jordan · Martin J Wainwright · Andre Wibisono -
2012 Poster: A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation »
Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Arindam Banerjee -
2012 Poster: Exponential Concentration for Mutual Information Estimation with Application to Forests »
Han Liu · John Lafferty · Larry Wasserman -
2011 Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback »
Andreas Krause · Pradeep Ravikumar · Stefanie S Jegelka · Jeffrey A Bilmes -
2011 Workshop: Copulas in Machine Learning »
Gal Elidan · Zoubin Ghahramani · John Lafferty -
2011 Poster: On Learning Discrete Graphical Models using Greedy Methods »
Ali Jalali · Christopher C Johnson · Pradeep Ravikumar -
2011 Spotlight: On Learning Discrete Graphical Models using Greedy Methods »
Ali Jalali · Christopher C Johnson · Pradeep Ravikumar -
2011 Poster: A More Powerful Two-Sample Test in High Dimensions using Random Projection »
Miles Lopes · Laurent Jacob · Martin J Wainwright -
2011 Poster: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
Ambuj Tewari · Pradeep Ravikumar · Inderjit Dhillon -
2011 Poster: High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity »
Po-Ling Loh · Martin J Wainwright -
2011 Poster: Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar -
2011 Oral: High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity »
Po-Ling Loh · Martin J Wainwright -
2011 Session: Oral Session 5 »
Pradeep Ravikumar -
2011 Poster: Nearest Neighbor based Greedy Coordinate Descent »
Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka -
2010 Workshop: Robust Statistical Learning »
Pradeep Ravikumar · Constantine Caramanis · Sujay Sanghavi -
2010 Session: Oral Session 14 »
Pradeep Ravikumar -
2010 Spotlight: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2010 Spotlight: Graph-Valued Regression »
Han Liu · Xi Chen · John Lafferty · Larry Wasserman -
2010 Poster: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2010 Poster: Graph-Valued Regression »
Han Liu · Xi Chen · John Lafferty · Larry Wasserman -
2010 Oral: A Dirty Model for Multi-task Learning »
Ali Jalali · Pradeep Ravikumar · Sujay Sanghavi · Chao Ruan -
2010 Oral: Fast global convergence rates of gradient methods for high-dimensional statistical recovery »
Alekh Agarwal · Sahand N Negahban · Martin J Wainwright -
2010 Poster: Fast global convergence rates of gradient methods for high-dimensional statistical recovery »
Alekh Agarwal · Sahand N Negahban · Martin J Wainwright -
2010 Poster: A Dirty Model for Multi-task Learning »
Ali Jalali · Pradeep Ravikumar · Sujay Sanghavi · Chao Ruan -
2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes -
2009 Poster: Information-theoretic lower bounds on the oracle complexity of convex optimization »
Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright -
2009 Poster: Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness »
Garvesh Raskutti · Martin J Wainwright · Bin Yu -
2009 Spotlight: Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness »
Garvesh Raskutti · Martin J Wainwright · Bin Yu -
2009 Spotlight: Information-theoretic lower bounds on the oracle complexity of convex optimization »
Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright -
2009 Poster: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu -
2009 Oral: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu -
2008 Poster: Nonparametric regression and classification with joint sparsity constraints »
Han Liu · John Lafferty · Larry Wasserman -
2008 Poster: Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images »
Pradeep Ravikumar · Vincent Vu · Bin Yu · Thomas Naselaris · Kendrick Kay · Jack Gallant -
2008 Poster: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Poster: Phase transitions for high-dimensional joint support recovery »
Sahand N Negahban · Martin J Wainwright -
2008 Spotlight: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Spotlight: Nonparametric regression and classification with joint sparsity constraints »
Han Liu · John Lafferty · Larry Wasserman -
2008 Spotlight: Phase transitions for high-dimensional joint support recovery »
Sahand N Negahban · Martin J Wainwright -
2008 Spotlight: Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images »
Pradeep Ravikumar · Vincent Vu · Bin Yu · Thomas Naselaris · Kendrick Kay · Jack Gallant -
2008 Poster: Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \ell_1-regularizedMLE »
Pradeep Ravikumar · Garvesh Raskutti · Martin J Wainwright · Bin Yu -
2007 Poster: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Spotlight: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Spotlight: Statistical Analysis of Semi-Supervised Regression »
John Lafferty · Larry Wasserman -
2007 Spotlight: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: Statistical Analysis of Semi-Supervised Regression »
John Lafferty · Larry Wasserman -
2007 Poster: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: Compressed Regression »
Shuheng Zhou · John Lafferty · Larry Wasserman -
2007 Poster: Loop Series and Bethe Variational Bounds in Attractive Graphical Models »
Erik Sudderth · Martin J Wainwright · Alan S Willsky -
2006 Poster: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
Martin J Wainwright · Pradeep Ravikumar · John Lafferty