Sparsity

• Convex vs. nonconvex approaches for sparse estimation: GLasso, multiple kernel learning, and HGLasso,
  with A.Y. Aravkin, A. Chiuso, and G. Pillonetto, Journal of Machine Learning Research, 15(2014) 217-252.
• Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation.
  with A.Y. Aravkin and G. Pillonetto, In Compressed Sensing & Sparse Filtering, eds., A. Carmi, L. Mihaylova, and S. Godsill. Springer. pp. 237-281, 2014.
• On the MSE properties of empirical Bayes methods for sparse estimation,
  with A.Y.Aravkin, A.Chiuso, and G.Pillonetto, IFAC Systems Identification, Volume 16, Part 1, Pages 965-970, 2012.
• A statistical and computational theory for robust and sparse Kalman smoothing,
  with A.Y.Aravkin and G.Pillonetto, IFAC Systems Identification, Volume 16, Part 1, Pages 894-899, 2012.
• Convex vs. non-convex approaches to sparse estimation: LASSO, multiple kernel learning, and hyperparameter LASSO,
  with A.Y.Aravkin, A.Chiuso, and G.Pillonetto, Proceedings of the IEEE Conference on Decision and Control (CDC), 2011, pp. 156--161.
• A nonlinear sparsity promoting formulation and algorithm for full-waveform inversion,
  with A.Y.Aravkin, T. van Leeuwen, and F.J.Herrmann, 73rd EAGE Expanded Abstracts, 2011.