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We consider linear models for stochastic dynamics.
Robust and optimal control
K. Zhou, J.C. Doyle, and K. Glover · 1996
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Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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Spectral Graph Theory
F.R.K. Chung · 1997
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For most large underdetermined systems of equations, the minimal l1-norm near-solution approximates the sparsest near-solution
D.L. Donoho · 2006
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For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution
D.L. Donoho · 2006
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On model selection consistency of Lasso
P. Zhao and B. Yu · 2006
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Modeling and Simulating Chemical Reactions
K. Ball, T.G. Kurtz, L. Popovic, and G. Rempala · 2006
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Stochastic simulation of chemical kinetics
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High-Dimensional Graphical Model Selection Using l-1-Regularized Logistic Regression
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Sparse inverse covariance estimation with the graphical lasso
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High-dimensional Ising model selection using l1-regularized logistic regression
P. Ravikumar, M.J. Wainwright, and J. Lafferty · 2008
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Modeling and Simulating Chemical Reactions
T. Toni, D. Welch, N. Strelkova, A. Ipsen, and M.P.H. Stumpf · 2009
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Some sharp performance bounds for least squares regression with L1 regularization
T. Zhang · 2009
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Sharp thresholds for high-dimensional and noisy sparsity recovery using l1-constrained quadratic programming (Lasso)
M.J. Wainwright · 2009
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Learning and Inference in Computational Systems Biology
N.D.Lawrence et al., editor · 2010
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Graphical models of autoregressive processes
J. Songsiri, J. Dahl, and L. Vandenberghe · 2010
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Topology selection in graphical models of autoregressive processes
J. Songsiri and L. Vandenberghe · 2010
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