Graphical Models
Compact, visually appealing, and mathematically amenable representations of joint distributions over a large number of variables, using graphs.
Publications
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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.
On Poisson Graphical Models 2013
Eunho Yang, Pradeep Ravikumar, Genevera Allen and Zhandong Liu, In Advances in Neural Information Processing Systems (NIPS) 2013.
A Divide-and-Conquer Procedure for Sparse Inverse Covariance Estimation 2012
Cho-Jui Hsieh, Inderjit Dhillon, Pradeep Ravikumar, and Arindam Banerjee, NIPS (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.
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.
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 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.
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.
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.
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).
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.
A Hierarchical Graphical Model for Record Linkage 2004
P. Ravikumar and W. W. Cohen, In Uncertainty in Artificial Intelligence (UAI), pp. 454-461 2004.
Variational Chernoff bounds for graphical models 2004
P. Ravikumar and J. Lafferty, In Uncertainty in Artificial Intelligence (UAI), pp. 462-469 2004.