Pradeep Ravikumar
Faculty
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Conditional Random Fields via Univariate Exponential Families 2013
Eunho Yang, Pradeep Ravikumar, Genevera Allen and Zhandong Liu, In Advances in Neural Information Processing Systems (NIPS) 2013.
Dirty Statistical Models 2013
Eunho Yang and Pradeep Ravikumar, Advances in Neural Information Processing Systems (NIPS) (2013).
Human Boosting 2013
Harsh Pareek and Pradeep Ravikumar, ICML (2013).
On Poisson Graphical Models 2013
Eunho Yang, Pradeep Ravikumar, Genevera Allen and Zhandong Liu, In Advances in Neural Information Processing Systems (NIPS) 2013.
On Robust Estimation of High Dimensional Generalized Linear Models 2013
Eunho Yang, Ambuj Tewari and Pradeep Ravikumar, In International Joint Conference on Artificial Intelligence (IJCAI) 2013.
A Divide-and-Conquer Procedure for Sparse Inverse Covariance Estimation 2012
Cho-Jui Hsieh, Inderjit Dhillon, Pradeep Ravikumar, and Arindam Banerjee, NIPS (2012).
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers 2012
S. Negahban, P. Ravikumar, M. J. Wainwright, and B. Yu, Statistical Science (2012).
Graphical Models via Generalized Linear Models 2012
Eunho Yang, Pradeep Ravikumar, Genevera Allen, and Zhandong Liu, NIPS (2012).
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods 2012
Christopher Johnson, Ali Jalali, and Pradeep Ravikumar, In International Conference on AI and Statistics (AISTATS) 2012.
Information-theoretic lower bounds on the oracle complexity of convex optimization 2012
A. Agarwal, P. Bartlett, P. Ravikumar, and M. Wainwright, IEEE Transactions on Information Theory, Vol. 58, 5 (2012), pp. 3235-3249.
Perturbation based Large Margin Approach for Ranking 2012
Eunho Yang, Ambuj Tewari and Pradeep Ravikumar, In International Conference on Artificial Intelligence and Statistics (AISTATS) 2012.
Encoding and Decoding V1 fMRI Responses to Natural Images with Sparse Nonparametric Models 2011
V. Vu, P. Ravikumar, T. Naselaris, K. Kay, J. Gallant, and B. Yu, Annals of Applied Statistics (2011), pp. 1159-1182.
Greedy Algorithms for Structurally Constrained High Dimensional Problems 2011
A. Tewari, P. Ravikumar, and I. Dhillon, In Neural Information Processing Systems 2011.
High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence 2011
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu, Electronic Journal of Statistics, Vol. 5 (2011), pp. 935-980.
Nearest Neighbor based Greedy Coordinate Descent 2011
I. Dhillon, P. Ravikumar, and A. Tewari, In Neural Information Processing Systems 2011.
On Learning Discrete Graphical Models using Greedy Methods 2011
Ali Jalali, Christopher Johnson, and Pradeep Ravikumar, In Neural Information Processing Systems 2011.
On Learning Discrete Graphical Models using Group-Sparse Regularization 2011
A. Jalali, P. Ravikumar, V. Vasuki, and S. Sanghavi, In International Conference on AI and Statistics (AISTATS) 2011.
On NDCG Consistency of Listwise Ranking Methods 2011
Pradeep Ravikumar, Ambuj Tewari and Eunho Yang, International Conference on AI and Statistics (AISTATS) (2011).
On the Use of Variational Inference for Learning Discrete Graphical Models 2011
Eunho Yang and Pradeep Ravikumar, In International Conference on Machine learning (ICML) 2011.
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation 2011
C.-J. Hsieh, M. Sustik, I. Dhillon, and P. Ravikumar, In Neural Information Processing Systems 2011.
A Dirty Model for Multi-task Learning 2010
A. Jalali, P. Ravikumar, S. Sanghavi, and C. Ruan, In Neural Information Processing Systems 2010.
Information-theoretic lower bounds on the oracle complexity of sparse convex optimization 2010
A. Agarwal, P. Bartlett, P. Ravikumar, and M. Wainwright, In International Workshop on Optimization for Machine Learning (OPT) 2010.
Message-passing for graph-structured linear programs: proximal methods and rounding schemes 2010
P. Ravikumar, A. Agarwal, and M. J. Wainwright, Journal of Machine Learning Research (JMLR), Vol. 11 (2010), pp. 1043-1080.
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers 2009
S. Negahban, P. Ravikumar, M. J. Wainwright, and B. Yu, In Neural Information Processing Systems 2009.
Information-theoretic lower bounds on the oracle complexity of convex optimization 2009
A. Agarwal, P. Bartlett, P. Ravikumar, and M. Wainwright, In Neural Information Processing Systems 2009.
Sparse Additive Models 2009
P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman, Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB), Vol. 71, 5 (2009), pp. 1009-1030.
Message-passing for graph-structured linear programs: Proximal projections, convergence and rounding schemes 2008
P. Ravikumar, A. Agarwal, and M. J. Wainwright, In International Conference on Machine learning (ICML) 2008.
Model selection in Gaussian graphical models: High-dimensional consistency of l1-regularized MLE 2008
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu, In Neural Information Processing Systems 2008.
Nonparametric sparse hierarchical models describe V1 fmri responses to natural images 2008
P. Ravikumar, V. Vu, B. Yu, T. Naselaris, K. Kay, and J. Gallant, In Neural Information Processing Systems 2008.
Approximate inference, structure learning and feature estimation in Markov random fields 2007
P. Ravikumar, Technical Report CMU-ML-07-115, Ph.D. Thesis, Carnegie Mellon University (2007).
SpAM: sparse additive models 2007
P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman, In Neural Information Processing Systems 2007.
High-dimensional graphical model selection using l1-regularized logistic regression 2006
M. J. Wainwright, P. Ravikumar, and J. Lafferty, In Neural Information Processing Systems 2006.
Preconditioner approximations for probabilistic graphical models 2006
P. Ravikumar and J. Lafferty, In Neural Information Processing Systems, pp. 1113-1120 2006.
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation 2006
P. Ravikumar and J. Lafferty, In International Conference on Machine learning (ICML), pp. 737-744 2006.
Comments: The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers 2005
W. W. Cohen, S. Fienberg, and P. Ravikumar, Journal of Business and Economic Statistics, Vol. 23, 2 (2005), pp. 160-162.
A Hierarchical Graphical Model for Record Linkage 2004
P. Ravikumar and W. W. Cohen, In Uncertainty in Artificial Intelligence (UAI), pp. 454-461 2004.
A Secure Protocol for Computing String Distance Metrics 2004
P. Ravikumar, W. W. Cohen, and S. E. Fienberg, In In IEEE International Conference on Data Mining (ICDM) 04, Workshop on Privacy and Security Aspects of Data Mining 2004.
Variational Chernoff bounds for graphical models 2004
P. Ravikumar and J. Lafferty, In Uncertainty in Artificial Intelligence (UAI), pp. 462-469 2004.
A Comparison of String Distance Metrics for Name-Matching Tasks 2003
W. W. Cohen, P. Ravikumar, and S. Fienberg, In In International Joint Conference on Artificial Intelligence (IJCAI) 18, Workshop on Information Integration on the Web 2003.
A Comparison of String Metrics for Matching Names and Records 2003
W. W. Cohen, P. Ravikumar, and S. Fienberg, In International Conference on Knowledge Discovery and Data Mining (KDD) 09, Workshop on Data Cleaning, Record Linkage, and Object Consolidation 2003.
Adaptive Name-Matching in Information Integration 2003
Mikhail Bilenko, William W. Cohen, Stephen Fienberg, Raymond J. Mooney, and Pradeep Ravikumar, IEEE Intelligent Systems, Vol. 18, 5 (2003), pp. 16-23.
Currently affiliated with Statistical Learning