Related Events (a corresponding poster, oral, or spotlight)

• 2007 Spotlight: SpAM: Sparse Additive Models »
  Tue. Dec 4th 05:50 -- 06:00 PM Room

More from the Same Authors

• 2020 Poster: PLLay: Efficient Topological Layer based on Persistent Landscapes »
  Kwangho Kim · Jisu Kim · Manzil Zaheer · Joon Kim · Frederic Chazal · Larry Wasserman
• 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: Statistical Inference for Cluster Trees »
  Jisu KIM · Yen-Chi Chen · Sivaraman Balakrishnan · Alessandro Rinaldo · Larry Wasserman
• 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: Optimal Ridge Detection using Coverage Risk »
  Yen-Chi Chen · Christopher Genovese · Shirley Ho · Larry Wasserman
• 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: Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations »
  Kirthevasan Kandasamy · Akshay Krishnamurthy · Barnabas Poczos · Larry Wasserman · james m robins
• 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
• 2013 Poster: Cluster Trees on Manifolds »
  Sivaraman Balakrishnan · Srivatsan Narayanan · Alessandro Rinaldo · Aarti Singh · Larry Wasserman
• 2012 Workshop: Algebraic Topology and Machine Learning »
  Sivaraman Balakrishnan · Alessandro Rinaldo · Donald Sheehy · Aarti Singh · Larry Wasserman
• 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: Graphical Models via Generalized Linear Models »
  Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu
• 2012 Oral: Graphical Models via Generalized Linear Models »
  Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu
• 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: Philosophy and Machine Learning »
  Marcello Pelillo · Joachim M Buhmann · Tiberio Caetano · Bernhard Schölkopf · 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: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
  Ambuj Tewari · Pradeep Ravikumar · Inderjit Dhillon
• 2011 Poster: Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation »
  Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar
• 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: Graph-Valued Regression »
  Han Liu · Xi Chen · John Lafferty · Larry Wasserman
• 2010 Poster: Multivariate Dyadic Regression Trees for Sparse Learning Problems »
  Han Liu · Xi Chen
• 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 Poster: A Dirty Model for Multi-task Learning »
  Ali Jalali · Pradeep Ravikumar · Sujay Sanghavi · Chao Ruan
• 2010 Poster: Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models »
  Han Liu · Kathryn Roeder · Larry Wasserman
• 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 Spotlight: Information-theoretic lower bounds on the oracle complexity of convex optimization »
  Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright
• 2009 Poster: Nonparametric Greedy Algorithms for the Sparse Learning Problem »
  Han Liu · Xi Chen
• 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 Spotlight: Nonparametric regression and classification with joint sparsity constraints »
  Han Liu · John Lafferty · Larry Wasserman
• 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 Spotlight: Statistical Analysis of Semi-Supervised Regression »
  John Lafferty · Larry Wasserman
• 2007 Poster: Statistical Analysis of Semi-Supervised Regression »
  John Lafferty · Larry Wasserman
• 2007 Poster: Compressed Regression »
  Shuheng Zhou · John Lafferty · Larry Wasserman
• 2006 Poster: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
  Martin J Wainwright · Pradeep Ravikumar · John Lafferty
• 2006 Spotlight: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
  Martin J Wainwright · Pradeep Ravikumar · John Lafferty