I lead the Foundations of Statistical Machine Learning Group in the Machine Learning Department, School of Computer Science at Carnegie Mellon University.
I obtained my PhD from the School of Computer Science at Carnegie Mellon University in 2007, advised by John Lafferty, and was a postdoctoral scholar at the Department of Statistics, University of California, Berkeley through 2009, working with Martin Wainwright and Bin Yu.
I have received the Sloan Research Fellowship, the National Science Foundation’s CAREER Award, and the Siebel Scholarship.
I was Program Chair for the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2013.
Phd Thesis: Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields. Honorable Mention, ACM SIGKDD Dissertation Award. Honorable Mention, CMU School of Computer Science Distinguished Dissertation Award.
Publications
2017
- Doubly Greedy Primal-Dual Coordinate Methods for Sparse Empirical Risk Minimization (pdf, software)
Q. Lei, I. Yen, C. Wu, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML), pp. 8, August 2017. - A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution (arXiv, software, code)
D. Inouye, E. Yang, G. Allen, P. Ravikumar.
WIREs Computational Statistics , May 2017. - Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain (pdf, software)
X. Huang, I. Yen, R. Zhang, Q. Huang, P. Ravikumar, I. Dhillon.
In International Conference on Artificial Intelligence and Statistics (AISTATS), April 2017. - Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition (pdf, software)
J. Zhang, I. Yen, P. Ravikumar, I. Dhillon.
In International Conference on Artificial Intelligence and Statistics (AISTATS), April 2017.
2016
- Dual Decomposed Learning with Factorwise Oracle for Structural SVM with Large Output Domain (pdf, software)
I. Yen, X. Huang, K. Zhong, R. Zhang, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), December 2016. - Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies (pdf, arXiv, poster, software, code)
D. Inouye, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML), pp. 2445-2453, June 2016. (Oral) - A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery (pdf, slides, poster, software)
I. Yen, X. Lin, J. Zhang, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML), June 2016. (Oral) - A Primal and Dual Sparse Approach to Extreme Classification (pdf, slides, poster, software, code)
I. Yen, X. Huang, K. Zhong, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML), June 2016. (Oral)
2015
- Consistent Multilabel Classification
O. Koyejo, N. Natarajan, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), pp. 3303–3311, December 2015. - Collaborative Filtering with Graph Information: Consistency and Scalable Methods (pdf, software, code)
N. Rao, H. Yu, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), pp. 2098–2106, December 2015. (Spotlight) - Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs (pdf, software)
V. Sivakumar, A. Banerjee, P. Ravikumar.
In Neural Information Processing Systems (NIPS), pp. 2206-2214, December 2015. - Closed-form Estimators for High-dimensional Generalized Linear Models (pdf, software)
E. Yang, A. Lozano, P. Ravikumar.
To appear in Neural Information Processing Systems (NIPS), pp. 586-594, December 2015. (Spotlight) - Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent (pdf, software, code)
I. Yen, K. Zhong, C. Hsieh, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), December 2015. - Fast Classification Rates for High-dimensional Gaussian Generative Models (pdf, software)
T. Li, A. Prasad, P. Ravikumar.
In Neural Information Processing Systems (NIPS), pp. 1054-1062, December 2015. - Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial (pdf, poster, software, code)
D. Inouye, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), pp. 3195-3203, December 2015. - Graphical Models via Univariate Exponential Family Distributions (pdf, arXiv, software)
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
Journal of Machine Learning Research (JMLR) , pp. 3813-3847, December 2015. - A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (pdf, slides, software)
I. Yen, X. Lin, K. Zhong, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML), pp. 2418-2426, July 2015. - Distributional Rank Aggregation, and an Axiomatic Analysis (pdf, software)
A. Prasad, H. Pareek, P. Ravikumar.
In International Conference on Machine Learning (ICML), July 2015. - Vector-Space Markov Random Fields via Exponential Families (pdf, arXiv, software)
W. Tansey, O. Padilla, A. Suggala, P. Ravikumar.
In International Conference on Machine Learning (ICML), pp. 684-692, July 2015. - Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics (pdf, arXiv, software)
N. Natarajan, O. Koyejo, P. Ravikumar, I. Dhillon.
arXiv (arXiv) 1505.01802, May 2015. - Learning-based Analytical Cross-Platform Performance Prediction (pdf, software)
X. Zheng, P. Ravikumar, L. John, A. Gerstlauer.
In International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, 2015. - Sparsistency of l1-Regularized M-Estimators (pdf, arXiv, software)
Y. Li, J. Scarlett, P. Ravikumar, V. Cevher.
In International Conference on Artificial Intelligence and Statistics (AISTATS), 2015. (Oral) - Tracking with Ranked Signals (pdf, software)
T. Li, H. Pareek, P. Ravikumar, D. Balwada, K. Speer.
In Conference on Uncertainity in Artificial Intelligence (UAI) (UAI), 2015. (Oral)
2014
- Predicting Growth Conditions from Internal Metabolic Fluxes in an In-Silico Model of E. coli (pdf, software)
V. Sridhara, A. Meyer, P. Rai, J. Barrick, P. Ravikumar, D. Segrè, C. Wilke.
PLoS ONE 9(12), pp. e114608, December 2014. - Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators (pdf, software, code)
K. Zhong, I. Yen, I. Dhillon, P. Ravikumar.
In Neural Information Processing Systems (NIPS), pp. 2375-2383, December 2014. - A Representation Theory for Ranking Functions (pdf, software)
H. Pareek, P. Ravikumar.
In Neural Information Processing Systems (NIPS), December 2014. - Consistent Binary Classification with Generalized Performance Metrics (pdf, poster, software)
N. Natarajan, O. Koyejo, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), pp. 2744-2752, December 2014. - Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings (pdf, software)
I. Yen, C. Hsieh, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), pp. 1008-1016, December 2014. - Elementary Estimators for Graphical Models (pdf, software)
E. Yang, A. Lozano, P. Ravikumar.
In Neural Information Processing Systems (NIPS), December 2014. - On the Information Theoretic Limits of Learning Ising Models (arXiv, software)
R. Tandon, K. Shanmugam, A. Dimakis, P. Ravikumar.
To appear in Neural Information Processing Systems (NIPS), December 2014. - QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models (pdf, software)
C. Hsieh, I. Dhillon, P. Ravikumar, S. Becker, P. Olsen.
In Neural Information Processing Systems (NIPS), pp. 2006–2014, December 2014. - Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space (pdf, software)
I. Yen, T. Lin, S. Lin, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), pp. 2456-2464, December 2014. - Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs (pdf, poster, software, code)
D. Inouye, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), pp. 3158-3166, December 2014. - QUIC: Quadratic Approximation for Sparse Inverse Covariance Matrix Estimation (pdf, software)
C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
Journal of Machine Learning Research (JMLR), October 2014. - Elementary Estimators for High-Dimensional Linear Regression (pdf, software)
E. Yang, A. Lozano, P. Ravikumar.
In International Conference on Machine Learning (ICML), 2014. (Oral) - Elementary Estimators for Sparse Covariance Matrices and other Structured Moments (pdf, software)
E. Yang, A. Lozano, P. Ravikumar.
In International Conference on Machine Learning (ICML), 2014. (Oral) - Learning Graphs with a Few Hubs (pdf, software)
R. Tandon, P. Ravikumar.
In International Conference on Machine Learning (ICML), June 2014. - Exponential Family Matrix Completion under Structural Constraint (pdf, software)
S. Gunasekar, P. Ravikumar, J. Ghosh.
In International Conference on Machine Learning (ICML), June 2014. - Admixture of Poisson MRFs: A Topic Model with Word Dependencies (pdf, slides, poster, software, code)
D. Inouye, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML), pp. 683-691, June 2014. - Mixed Graphical Models via Exponential Families (pdf, software)
E. Yang, Y. Baker, P. Ravikumar, G. Allen, Z. Liu.
In International Conference on Artificial Intelligence and Statistics (AISTATS), 2014. (Oral)
2013
- Dirty Statistical Models (pdf, software)
E. Yang, P. Ravikumar.
In Neural Information Processing Systems (NIPS), 2013. - Conditional Random Fields via Univariate Exponential Families (pdf, software)
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Neural Information Processing Systems (NIPS), December 2013. - On Poisson Graphical Models (pdf, software)
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Neural Information Processing Systems (NIPS), December 2013. - BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables (pdf, software)
C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar, R. Poldrack.
In Neural Information Processing Systems (NIPS), December 2013. (Oral) - Large Scale Distributed Sparse Precision Estimation (pdf, software)
H. Wang, C. Hsieh, A. Banerjee, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), December 2013. - Learning with Noisy Labels (pdf, poster, software)
N. Natarajan, A. Tewari, I. Dhillon, P. Ravikumar.
In Neural Information Processing Systems (NIPS), pp. 1196-1204, December 2013. - On the Difficulty of Learning Power Law Graphical Models (pdf, software)
R. Tandon, P. Ravikumar.
In IEEE International Symposium on Information Theory (ISIT), 2013. - A Dirty Model for Multiple Sparse Regression (pdf, software)
A. Jalali, P. Ravikumar, S. Sanghavi.
IEEE Transactions on Information Theory 59(12), pp. 7947-7968, 2013. - Human Boosting (pdf, software)
H. Pareek, P. Ravikumar.
In International Conference on Machine Learning (ICML), 2013. - On Robust Estimation of High Dimensional Generalized Linear Models (pdf, software)
E. Yang, A. Tewari, P. Ravikumar.
In International Joint Conference on Artificial Intelligence (IJCAI), 2013.
2012
- A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation (pdf, software)
C. Hsieh, I. Dhillon, P. Ravikumar, A. Banerjee.
In Neural Information Processing Systems (NIPS), pp. 2339-2347, December 2012. - Graphical Models via Generalized Linear Models (pdf, software)
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Neural Information Processing Systems (NIPS), 2012. (Oral) - A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers (pdf, software)
S. Negahban, P. Ravikumar, M. Wainwright, B. Yu.
Statistical Science 27(4), pp. 538-557, 2012. - High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods (pdf, software)
A. Jalali, C. Johnson, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS), 2012. - Information-theoretic lower bounds on the oracle complexity of convex optimization (pdf, software)
A. Agarwal, P. Bartlett, P. Ravikumar, M. Wainwright.
IEEE Transactions on Information Theory 58(5), pp. 3235-3249, 2012. - Perturbation based Large Margin Approach for Ranking (pdf, software)
E. Yang, A. Tewari, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.
2011
- Greedy Algorithms for Structurally Constrained High Dimensional Problems (pdf, software)
A. Tewari, P. Ravikumar, I. Dhillon.
In Neural Information Processing Systems (NIPS), December 2011. - Nearest Neighbor based Greedy Coordinate Descent (pdf, software)
I. Dhillon, P. Ravikumar, A. Tewari.
In Neural Information Processing Systems (NIPS), December 2011. - Sparse Inverse Covariance Matrix Estimation using Quadratic Approximation (pdf, software)
C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
In Neural Information Processing Systems (NIPS), December 2011. - On Learning Discrete Graphical Models using Group-Sparse Regularization (pdf, software)
A. Jalali, P. Ravikumar, V. Vasuki, S. Sanghavi.
In International Conference on Artificial Intelligence and Statistics (AISTATS), April 2011. - High dimensional covariance estimation by minimizing l1-penalized log-determinant divergence (pdf, software)
P. Ravikumar, M. Wainwright, B. Yu, G. Raskutti.
Electronic Journal of Statistics (EJS) 5, pp. 935-980, 2011. - On NDCG Consistency of Listwise Ranking Methods (pdf, software)
P. Ravikumar, A. Tewari, E. Yang.
In International Conference on Artificial Intelligence and Statistics (AISTATS), 2011. - On the Use of Variational Inference for Learning Discrete Graphical Models (pdf, software)
E. Yang, P. Ravikumar.
In International Conference on Machine Learning (ICML), 2011. (Oral) - On Learning Discrete Graphical Models using Greedy Methods (pdf, software)
A. Jalali, C. Johnson, P. Ravikumar.
In Neural Information Processing Systems (NIPS), 2011. - Encoding and Decoding V1 fMRI Responses to Natural Images with Sparse Nonparametric Models (pdf, arXiv, software)
V. Vu, P. Ravikumar, T. Naselaris, K. Kay, J. Gallant, B. Yu.
Annals of Applied Statistics 5, pp. 1159-1182, 2011.
2010
- A Dirty Model for Multi-task Learning (pdf, software)
A. Jalali, P. Ravikumar, S. Sanghavi, C. Ruan.
In Neural Information Processing Systems (NIPS), December 2010. - Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes (pdf, software)
P. Ravikumar, A. Agarwal, M. Wainwright.
Journal of Machine Learning Research (JMLR) 11, pp. 1043-1080, March 2010. - High-dimensional Ising Model Selection using ℓ1-Regularized Logistic Regression (pdf, arXiv, software)
P. Ravikumar, M. Wainwright, J. Lafferty.
Annals of Statistics (Ann. Statist.) 38, pp. 1287-1319, 2010.
2009
- Error-Correcting Tournaments (pdf, arXiv, software)
A. Beygelzimer, J. Langford, P. Ravikumar.
In International Conference on Algorithmic Learning Theory (ALT), 2009. - Sparse Additive Models (pdf, arXiv, software)
P. Ravikumar, J. Lafferty, H. Liu, L. Wasserman.
Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB) 71(5), pp. 1009-1030, 2009.
2008
- Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-Regularized MLE (pdf, software)
P. Ravikumar, G. Raskutti, M. Wainwright, B. Yu.
In Neural Information Processing Systems (NIPS), December 2008. - Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images (pdf, software)
P. Ravikumar, V. Vu, B. Yu, T. Naselaris, K. Kay, J. Gallant.
In Neural Information Processing Systems (NIPS), December 2008. - Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes (pdf, software)
P. Ravikumar, A. Agarwal, M. Wainwright.
In International Conference on Machine Learning (ICML), pp. 800-807, July 2008. - Single Index Convex Experts: Efficient Estimation via Adapted Bregman Losses (pdf, software)
P. Ravikumar, M. Wainwright, B. Yu.
Learning Workshop, Snowbird, 2008.