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.