Publications

Statistical Machine Learning

 

» A Representation Theory for Ranking Functions.
H. Pareek, P. Ravikumar.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» Elementary Estimators for Graphical Models.
E. Yang, A. Lozano, P. Ravikumar.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» On the Information Theoretic Limits of Learning Ising Models.
R. Tandon, K. Shanmugam, P. Ravikumar, A. Dimakis.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings.
I. En-Hsu Yen, C.-J. Hsieh, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» Sparse Random Feature Algorithms as Coordinate Descent in Hilbert Space.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» Consistent Binary Classification with Generalized Performance Metrics.
N. Nagarajan, S. Koyejo, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs.
D. Inouye, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models.
C.-J. Hsieh, I. Dhillon, P. Ravikumar, S. Becker, P. Olsen .
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» Proximal Quasi-Newton for Computationally Intensive l1-regularized M-estimators.
K. Zhong, I. En-Hsu Yen, I. Dhillon, P. Ravikumar.
In Advances in Neural Information Processing Systems (NIPS) 27, 2014 (to appear).

» QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation.
C.-J. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
Journal of Machine Learning Research (JMLR), 2014 (to appear).

» Elementary Estimators for High-Dimensional Linear Regression.
E. Yang, A. Lozano, P. Ravikumar.
In International Conference on Machine Learning (ICML) 31, 2014.

» Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.
E. Yang, A. Lozano, P. Ravikumar.
In International Conference on Machine Learning (ICML) 31, 2014.

» Exponential Family Matrix Completion under Structural Constraints [Appendix].
S. Gunasekar, P. Ravikumar, J. Ghosh.
In International Conference on Machine Learning (ICML) 31, 2014.

» Admixtures of Poisson MRFs: A Topic Model with Word Dependencies [Appendix].
D. Inouye, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML) 31, 2014.

» Learning Graphs with a Few Hubs [Appendix].
R. Tandon, P. Ravikumar.
In International Conference on Machine Learning (ICML) 31, 2014.

» Mixed Graphical Models via Exponential Families [Appendix].
E. Yang, Y. Baker, P. Ravikumar, G. Allen, Z. Liu.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 17, 2014.

» BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables.
C.-J. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar, R. Poldrack.
In Advances in Neural Information Processing Systems (NIPS) 26, 2013 (Oral).

» Dirty Statistical Models.
E. Yang, P. Ravikumar.
In Advances in Neural Information Processing Systems (NIPS) 26, 2013.

» On Poisson Graphical Models.
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Advances in Neural Information Processing Systems (NIPS) 26, 2013.

» Conditional Random Fields via Univariate Exponential Families.
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Advances in Neural Information Processing Systems (NIPS) 26, 2013.

» Learning with Noisy Labels.
N. Natarajan, I. Dhillon, P. Ravikumar, A. Tewari.
In Advances in Neural Information Processing Systems (NIPS) 26, 2013.

» Large Scale Distributed Sparse Precision Estimation.
H. Wang, A. Banerjee, C.-J. Hsieh, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NIPS) 26, 2013.

» On the Difficulty of Learning Power Law Graphical Models.
R. Tandon, P. Ravikumar.
In IEEE International Symposium on Information Theory (ISIT), 2013.

» On Robust Estimation of High Dimensional Generalized Linear Models.
E. Yang, A. Tewari, P. Ravikumar.
In International Joint Conference on Artificial Intelligence (IJCAI) 13, 2013.

» A Dirty Model for Multiple Sparse Regression.
A. Jalali, P. Ravikumar, S. Sanghavi.
IEEE Transactions on Information Theory, Vol. 59, No. 12, pages 7947-7968, 2013.

» Human Boosting.
H. Pareek, P. Ravikumar.
In International Conference on Machine Learning (ICML) 30, 2013.

» Graphical Models via Generalized Linear Models.
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Advances in Neural Information Processing Systems (NIPS) 25, 2012 (Oral).

» A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation.
C.-J. Hsieh, I. Dhillon, P. Ravikumar, A. Banerjee.
In Advances in Neural Information Processing Systems (NIPS) 25, 2012.

» Perturbation based Large Margin Approach for Ranking.
E. Yang, A. Tewari, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 15, 2012.

» High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods.
A. Jalali, C. Johnson, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 15, 2012.

» A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers.
S. Negahban, P. Ravikumar, M. J. Wainwright and B. Yu.
Statistical Science, Vol. 27, No. 4, pages 538-557, 2012.

» Information-theoretic lower bounds on the oracle complexity of convex optimization.
A. Agarwal, P. Bartlett, P. Ravikumar, M. Wainwright.
IEEE Transactions on Information Theory, Vol. 58, No. 5, pages 3235-3249, 2012.

» On Learning Discrete Graphical Models using Greedy Methods.
A. Jalali, C. Johnson, P. Ravikumar.
In Advances in Neural Information Processing Systems (NIPS) 24, 2011.

» Greedy Algorithms for Structurally Constrained High Dimensional Problems.
A. Tewari, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NIPS) 24, 2011.

» Nearest Neighbor based Greedy Coordinate Descent.
I. Dhillon, P. Ravikumar, A. Tewari.
In Advances in Neural Information Processing Systems (NIPS) 24, 2011.

» Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation.
C.-J. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
In Advances in Neural Information Processing Systems (NIPS) 24, 2011.

» High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence.
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu.
Electronic Journal of Statistics, Vol. 5, Pages 935-980, 2011.

» On the Use of Variational Inference for Learning Discrete Graphical Models.
E. Yang and P. Ravikumar.
In International Conference on Machine learning (ICML) 28, 2011.

» Encoding and Decoding V1 fMRI Responses to Natural Images with Sparse Nonparametric Models .
V. Vu, P. Ravikumar, T. Naselaris, K. Kay, J. Gallant and B. Yu.
Annals of Applied Statistics, Vol. 5, No. 2B, pages 1159-1182, 2011.

» On NDCG Consistency of Listwise Ranking Methods.
P. Ravikumar, A. Tewari, E. Yang.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 14, 2011.

» On Learning Discrete Graphical Models using Group-Sparse Regularization.
A. Jalali, P. Ravikumar, V. Vasuki, S. Sanghavi.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 14, 2011.

» A Dirty Model for Multi-task Learning[Appendix].
A. Jalali, P. Ravikumar, S. Sanghavi, C. Ruan.
In Advances in Neural Information Processing Systems (NIPS) 23, 2010 (Oral).

» Information-theoretic lower bounds on the oracle complexity of sparse convex optimization.
A. Agarwal, P. Bartlett, P. Ravikumar, M. Wainwright.
In International Workshop on Optimization for Machine Learning (OPT) 3, 2010.

» Message-passing for graph-structured linear programs: proximal methods and rounding schemes.
P. Ravikumar, A. Agarwal, and M. J. Wainwright.
Journal of Machine Learning Research (JMLR), Vol. 11, Pages 1043-1080, March 2010.

» High-dimensional Ising model selection using l1-regularized logistic regression.
P. Ravikumar, M. J. Wainwright and J. Lafferty.
Annals of Statistics, Vol. 38, Number 3, Pages 1287-1319, 2010.

» A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers.
S. Negahban, P. Ravikumar, M. J. Wainwright and B. Yu.
In Advances in Neural Information Processing Systems (NIPS) 23, 2009 (Oral).

» Information-theoretic lower bounds on the oracle complexity of convex optimization.
A. Agarwal, P. Bartlett, P. Ravikumar, M. Wainwright.
In Advances in Neural Information Processing Systems (NIPS) 22, 2009.

» Error-correcting tournaments.
Alina Beygelzimer, John Langford, and Pradeep Ravikumar.
In International Conference on Algorithmic Learning Theory (ALT) 20, 2009.

» Sparse Additive Models.
P. Ravikumar, J. Lafferty, H. Liu and L. Wasserman.
Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB), Vol. 71(5), pages 1009-1030, 2009.

» Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes.
In International Conference on Machine learning (ICML) 25, pages 800-807, 2008.

» Model selection in Gaussian graphical models: High-dimensional consistency of l1-regularized MLE.
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu.
In Advances in Neural Information Processing Systems (NIPS) 21, 2008.

» Nonparametric sparse hierarchical models describe V1 fmri responses to natural images.
P. Ravikumar, V. Vu, B. Yu, T. Naselaris, K. Kay, and J. Gallant.
In Advances in Neural Information Processing Systems (NIPS) 21, 2008.

» Single Index Convex Experts: Efficient Estimation via Adapted Bregman Losses.
P. Ravikumar, M. J. Wainwright and B. Yu.
Presented at the Learning Workshop, Snowbird 2008.

» Approximate inference, structure learning and feature estimation in Markov random fields.
P. Ravikumar.
Technical Report CMU-ML-07-115, Ph.D. Thesis, Carnegie Mellon University, 2007.
ACM SIGKDD Explorations, Vol. 10(2), pages 32-33, December 2008.

» High-Dimensional Graphical Model Selection Using l1-Regularized Logistic Regression.
M. J. Wainwright, P. Ravikumar and J. Lafferty.
In Advances in Neural Information Processing Systems (NIPS) 19, pages 1465-1472, 2007.

» Quadratic programming relaxations for metric labeling and Markov random field map estimation.
P. Ravikumar and J. Lafferty.
In International Conference on Machine learning (ICML) 23, pages 737-744, 2006.

» Preconditioner approximations for probabilistic graphical models.
P. Ravikumar and J. Lafferty.
In Advances in Neural Information Processing Systems (NIPS) 18, pages 1113-1120, 2006 (Oral)

» Variational Chernoff bounds for graphical models.
P. Ravikumar and J. Lafferty.
In Uncertainty in Artificial Intelligence (UAI) 20, pages 462-469, 2004.

» A Hierarchical Graphical Model for Record Linkage.
P. Ravikumar and W. W. Cohen.
In Uncertainty in Artificial Intelligence (UAI) 20, pages 454-461, 2004.


Information Integration


» Comments: The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers.
W. W. Cohen, S. Fienberg, P. Ravikumar.
Journal of Business and Economic Statistics, Vol. 23(2), pages 160-162, 2005.

» A Secure Protocol for Computing String Distance Metrics.
P. Ravikumar, W. W. Cohen, S. E. Fienberg.
In IEEE International Conference on Data Mining (ICDM) 04, Workshop on Privacy and Security Aspects of Data Mining, 2004.

» A Comparison of String Metrics for Matching Names and Records.
W. W. Cohen, P. Ravikumar, S. Fienberg.
In ACM 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.
M. Bilenko, R. Mooney, W. W. Cohen, P. Ravikumar, S. Fienberg.
IEEE Intelligent Systems, Vol. 18(5), pages 16-23, 2003.