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State-space smoothing has found many applications in science and engineering.
A New Approach to Linear Filtering and Prediction Problems
R. E. Kalman · 1960
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New results in linear filtering and prediction theory
R. E. Kalman and R. S. Bucy · 1961
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Maximum Likelihood estimates of linear dynamic systems
H. E. Rauch, F. Tung, and C. T. Striebel · 1965
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A solution of the smoothing problem for linear dynamic systems
D. Q. Mayne · 1966
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The optimum linear smoother as a combination of two optimum linear filters
D. C. Fraser and J. E. Potter · 1969
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Stochastic Processes and Filtering Theory
A. Jazwinski · 1970
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Convex Analysis
R. T. Rockafellar and R. T. Rochafellar · 1970
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Aurmented Lagrangian multiplier functions and duality in nonconvex programming
R. T. Rockafellar · 1974
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A unified approach to smoothing formulas
L. Ljung and T. Kailath · 1976
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Least Squares Estimation of Discrete Linear Dynamic Systems Using Orthogonal Transformations
C. C. Paige and M. A. Saunders · 1977
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Solutions of Ill-Posed Problems
A. Tikhonov and V. Arsenin · 1977
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Regression quantiles
R. Koenker and G. Bassett Jr · 1978
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Generalized cross-validation as a method for choosing a good ridge parameter
G. Golub, M. Heath, and G. Wahba · 1979
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Splitting algorithms for the sum of two nonlinear operators
P.-L. Lions and B. Mercier · 1979
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Ergodic convergence to a zero of the sum of monotone operators in hilbert space
G. B. Passty · 1979
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Robust Statistics
P. J. Huber · 1981
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A geometrical derivation of the fixed interval smoothing algorithm
C. F. Ansley and R. Kohn · 1982
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Descent methods for composite nondifferentiable optimization problems
J. Burke · 1985
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A smoothness priors time-varying ar coefficient modeling of nonstationary covariance time series
G. Kitagawa and W. Gersch · 1985
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Robust Statistics
F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel · 1986
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Choice of smoothing parameter in deconvolution problems
J. Rice · 1986
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Robust Preprocessing for {Kalman} Filtering of Glint Noise
G. A. Hewer, R. D. Martin, and J. Zeh · 1987
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Linear inverse and ill-posed problems
M. Bertero · 1989
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Empirical Bayes Method
J. S. Maritz and T. Lwin · 1989
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Spline Models For Observational Data
G. Wahba · 1990
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A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems
M. Kojima, N. Megiddo, T. Noma, and A. Yoshise · 1991
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The iterated Kalman filter update as a Gauss-Newton method
B. Bell and F. Cathey · 1993
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The Iterated {Kalman} Smoother as a {Gauss-Newton} Method
B. Bell · 1994
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Bayesian non-linear modelling for the prediction competition
D. Mackay · 1994
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Interior-Point Polynomial Algorithms in Convex Programming
A. Nemirovskii and Y. Nesterov · 1994
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A gauss?newton method for convex composite optimization
J. V. Burke and M. C. Ferris · 1995
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Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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Support vector regression machines
H. Drucker, C. Burges, L. Kaufman, A. Smola, and V. Vapnik · 1997
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Primal-Dual Interior-Point Methods
S. J. Wright · 1997
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Variational Analysis
R. T. Rockafellar and R. J. B. Wets · 1998
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Nonlinear Programming
D. Bertsekas · 1999
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System Identification - Theory for the User
L. Ljung · 1999
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The Marginal Likelihood for Parameters in a Discrete {Gauss-Markov} Process
B. M. Bell · 2000
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On the noise model of support vector machines regression
M. Pontil, S. Mukherjee, and F. Girosi · 2000
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Linear estimation
A. S. T. Kailath and B. Hassibi · 2000
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The unscented kalman filter for nonlinear estimation
E. A. Wan and R. Van Der Merwe · 2000
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A unified loss function in bayesian framework for support vector regression
W. Chu, S. S. Keerthi, and C. J. Ong · 2001
Cited alongside, same era.
The Elements of Statistical Learning. Data Mining, Inference and Prediction
T. Hastie, R. Tibshirani, and J. Friedman · 2001
Cited alongside, same era.
Kalman filtering and neural networks
S. S. Haykin, S. S. Haykin, and S. S. Haykin · 2001
Cited alongside, same era.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
B. Schölkopf and A. J. Smola · 2001
Cited alongside, same era.
Group Lasso with Overlap and Graph Lasso
L. Jacob, G. Obozinski, and J. P. Vert · 2009
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ℓ 1 \ell_{1} trend filtering
S. Kim, K. Koh, S. Boyd, and D. Gorinevsky · 2009
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Taking advantage of sparsity in multi-task learning
K. Lounici, M. Pontil, A. B. Tsybakov, and S. van de Geer · 2009
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Lasso-type recovery of sparse representations for high-dimensional data
N. Meinshausen and B. Yu · 2009
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Simultaneous support recovery in high-dimensional regression: Benefits and perils of ℓ 1 , ∞ \ell_{1,\infty} -regularization
S. Negahban and M. J. Wainwright · 2009
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On the conditions used to prove oracle results for the lasso
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A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking
M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp · 2002
Cited alongside, same era.
Kalman filtering with state equality constraints
D. Simon and T. L. Chia · 2002
Cited alongside, same era.
Handbook of Heavy Tailed Distributions in Finance
S. T. Rachev, editor · 2003
Cited alongside, same era.
Convex Optimization
S. Boyd and L. Vandenberghe · 2004
Cited alongside, same era.
Least angle regression
B. Efron, T. Hastie, L. Johnstone, and R. Tibshirani · 2004
Cited alongside, same era.
Robust Statistics
P. J. Huber · 2004
Cited alongside, same era.
S. van de Geer and P. Buhlmann · 2009
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Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (Lasso)
M. J. Wainwright · 2009
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Grouped and hierarchical model selection through composite absolute penalties
P. Zhao, G. Rocha, and B. Yu · 2009
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Regularization paths for generalized linear models via coordinate descent
J. Friedman, T. Hastie, and R. Tibshirani · 2010
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The bayesian elastic net
Q. Li, N. Lin, et al · 2010
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Union support recovery in high-dimensional multivariate regression
G. Obozinski, M. J. Wainwright, and M. I. Jordan · 2010
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Kalman filtering with state constraints: a survey of linear and nonlinear algorithms
D. Simon · 2010
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An outlier-robust kalman filter
G. Agamennoni, J. Nieto, and E. Nebot · 2011
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An ℓ 1 \ell_{1} -Laplace robust Kalman smoother
A. Aravkin, B. M. Bell, J. V. Burke, and G. Pillonetto · 2011
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An-laplace robust kalman smoother
A. Y. Aravkin, B. M. Bell, J. V. Burke, and G. Pillonetto · 2011
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Convex Analysis and Monotone Operator Theory in Hilbert Spaces
H. H. Bauschke and P. L. Combettes · 2011
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Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
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A first-order primal-dual algorithm for convex problems with applications to imaging
A. Chambolle and T. Pock · 2011
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Bayesian kalman filtering, regularization and compressed sampling
S. Chan, B. Liao, and K. Tsui · 2011
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Proximal splitting methods in signal processing
P. L. Combettes and J.-C. Pesquet · 2011
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Analysis of fixed-point and coordinate descent algorithms for regularized kernel methods
F. Dinuzzo · 2011
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Doubly Robust Smoothing of Dynamical Processes via Outlier Sparsity Constraints
S. Farahmand, G. B. Giannakis, and D. Angelosante · 2011
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Latent variable bayesian models for promoting sparsity
D. P. Wipf, B. D. Rao, and S. Nagarajan · 2011
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Interior point algorithms: theory and analysis
Y. Ye · 2011
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Robust inversion, dimensionality reduction, and randomized sampling
A. Aravkin, M. Friedlander, F. Herrmann, and T. van Leeuwen · 2012
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Large-scale linear support vector regression
C. Ho and C. Lin · 2012
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Smoothed state estimates under abrupt changes using sum-of-norms regularization
H. Ohlsson, F. Gustafsson, L. Ljung, and S. Boyd · 2012
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Kalman smoothing and block tridiagonal systems: new connections and numerical stability results
A. Y. Aravkin, B. B. Bell, J. V. Burke, and G. Pillonetto · 2013
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Generalized system identification with stable spline kernels
A. Y. Aravkin, J. V. Burke, and G. Pillonetto · 2013
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Sparse/robust estimation and kalman smoothing with nonsmooth log-concave densities: Modeling, computation, and theory
A. Y. Aravkin, J. V. Burke, and G. Pillonetto · 2013
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Robust derivative-free kalman filter based on huber’s m-estimation methodology
L. Chang, B. Hu, G. Chang, and A. Li · 2013
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A stochastic controller for vector linear systems with additive Cauchy noise
J. Fernándes, J. L. Speyer, and M. Idan · 2013
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Regularized m-estimators with nonconvexity: Statistical and algorithmic theory for local optima
P.-L. Loh and M. J. Wainwright · 2013
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New approach to noncausal identification of nonstationary stochastic fir systems subject to both smooth and abrupt parameter changes
M. Niedzwiecki and S. Gackowski · 2013
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Identification of switched linear regression models using sum-of-norms regularization
H. Ohlsson and L. Ljung · 2013
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Convex vs. nonconvex approaches for sparse estimation: GLASSO, multiple kernel learning, and HGLASSO
A. Aravkin, J. Burke, A. Chiuso, and G. Pillonetto · 2014
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Orthogonal matching pursuit for sparse quantile regression
A. Aravkin, A. Lozano, R. Luss, and P. Kambadur · 2014
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Robust and trend-following student’s t kalman smoothers
A. Y. Aravkin, J. V. Burke, and G. Pillonetto · 2014
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Faster convergence rates of relaxed peaceman-rachford and admm under regularity assumptions
D. Davis and W. Yin · 2014
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A three-operator splitting scheme and its optimization applications
D. Davis and W. Yin · 2015
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