Graphical Models

• 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.
• 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)
• 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.
• 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.
• 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.
• 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: 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.
• Learning Graphs with a Few Hubs (pdf, software)
  R. Tandon, P. Ravikumar.
  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)
• 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.
• 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 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)
• 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 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)