Selected Publications

Theoretical foundations for learning (Gaussian) graphical models:
  • O. Banerjee, L. E. Ghaoui, and A. d'Aspremont. Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data. Journal of Machine Learning Research, 2008.
  • P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu. High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence. Electronic Journal of Statistics, 2011.
  • T. Cai, W. Liu, and X. Luo. A constrained l1 minimization approach to sparse precision matrix estimation. Journal of American Statistical Association, 2011.
  • N. Meinshausen and P. Buhlmann. High dimensional graphs and variable selection with the lasso. Annals of Statistics, 2006.
  • Jean Honorio and Tommi Jaakkola. Inverse covariance estimation for high-dimensional data in linear time and space: Spectral methods for riccati and sparse models. In UAI, 2013.
  • T. Zhao and H. Liu. Sparse inverse covariance estimation with calibration. In NIPS, 2012.
  • M. Yuan. High dimensional inverse covariance matrix estimation via linear programming. JMLR, 2010.
Efficient techniques for learning high-dimensional Gaussian graphical models:
  • J. Friedman, T. Hastie, and R. Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 2008.
  • J. Duchi, S. Gould, and D. Koller. Projected subgradient methods for learning sparse Gaussians. In UAI, 2008.
  • Z. Lu. Smooth optimization approach for sparse covariance selection. SIAM Journal of Optimization, 2009.
  • A. d'Aspremont, O. Banerjee, and L. El Ghaoui. First-order methods for sparse covariance selection. SIAM Journal on Matrix Analysis and its Applications, 2008.
  • B. Rolfs, B. Rajaratnam, D. Guillot, A. Maleki, and I. Wong. Iterative thresholding algorithm for sparse inverse covariance estimation. In NIPS, 2012.
  • K. Scheinberg and I. Rish. Learning sparse Gaussian Markov networks using a greedy coordinate ascent approach. In Machine Learning and Knowledge Discovery in Databases, 2010.
  • K. Scheinberg, S. Ma, and D. Goldfarb. Sparse inverse covariance selection via alternating linearization methods. Advances in Neural Information Processing, 2010.
  • K. Scheinberg and X. Tang. Complexity of inexact proximal newton methods. Arxiv:1311.6547, 2013.
  • C.-J. Hsieh, M. A. Sustik, I. S. Dhillon, and P. Ravikumar. Sparse inverse covariance matrix estimation using quadratic approximation. In NIPS, 2011.
  • C.-J. Hsieh, I. S. Dhillon, P. Ravikumar, and A. Banerjee. A divide-and-conquer method for sparse inverse covariance estimation. In NIPS, 2012.
  • C.-J. Hsieh, M. A. Sustik, I. S. Dhillon, P. Ravikumar, and R. A. Poldrack. Big & Quic: Sparse inverse covariance estimation for a million variables. In NIPS, 2013.
  • H. Wang, A. Banerjee, C.-J. Hsieh, P. Ravikumar, and I. S. Dhillon. Large-scale disbributed sparse precision estimation. In NIPS, 2013.
  • P. Olsen, F. Oztoprak, J. Nocedal, and S. Rennie. Newton-like methods for sparse inverse covariance estimation. In NIPS, 2012.
  • X. Li, T. Zhao, and H. Liu. An r package flare for high dimensional linear regression and precision matrix estimation. 2013.
  • J. D. Lee, Y. Sun, and M. Saunders. Proximal Newton type methods for minimizing convex objective functions in composite form. In NIPS, 2012.
  • R. Mazumder and T. Hastie. Exact covariance thresholding into connected components for large-scale graphical lasso. JMLR, 2012.
  • D. M. Witten, J. H. Friedman, and N. Simon. New insights and faster computations for the graphical lasso. Journal of Computational and Graphical Statistics, 2011.
  • R. H. Byrd, J. Nocedal, and F. Oztoprak. An inexact successive quadratic approximation method for convex l-1 regularized optimization. Arxiv:1309.3529, 2013.
  • Q. T. Dinh, A. Kyrillidis, and V. Cevher. A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions. in ICML 2013.
Application oriented learning of graphical models and sparse inverse covariance models:
  • Erich Kummerfeld and David Danks. Tracking time-varying graphical structure. In NIPS 2013.
  • M. Kolar and E. P. Xing. Estimating sparse precision matrices from data with missing values. In ICML 2012.
  • N. Stadler and P. Buhlmann. Missing values: sparse inverse covariance estimation and an extension to sparse regression. Statistics and Computing, 2009.
  • W. Zhang and P. Fung. Sparse inverse covariance matrices for low resource speech recognition. IEEE Transactions on audio, speech and language processing, 2013.