Efficient stochastic optimisation by unadjusted Langevin Monte Carlo
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[1] E.J. Candes,et al. An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.
[2] E.J. Candes. Compressive Sampling , 2022 .
[3] Richard J. Boys,et al. Discussion to "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead , 2011 .
[4] Alexander Shapiro,et al. Stochastic Approximation approach to Stochastic Programming , 2013 .
[5] G. Casella. Empirical Bayes Gibbs sampling. , 2001, Biostatistics.
[6] Vishal Monga,et al. Handbook of Convex Optimization Methods in Imaging Science , 2017, Springer International Publishing.
[7] G. Pólya,et al. Problems and theorems in analysis , 1983 .
[8] M. Ledoux,et al. Analysis and Geometry of Markov Diffusion Operators , 2013 .
[9] Stephen P. Boyd,et al. An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.
[10] S. Meyn,et al. Stability of Markovian processes I: criteria for discrete-time Chains , 1992, Advances in Applied Probability.
[11] Antonin Chambolle,et al. An introduction to continuous optimization for imaging , 2016, Acta Numerica.
[12] H. Kushner,et al. Stochastic Approximation and Recursive Algorithms and Applications , 2003 .
[13] É. Moulines,et al. Convergence of a stochastic approximation version of the EM algorithm , 1999 .
[14] Gersende Fort,et al. Convergence of the Monte Carlo expectation maximization for curved exponential families , 2003 .
[15] Michael B. Wakin,et al. An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition] , 2008 .
[16] A. Eberle. Couplings, distances and contractivity for diffusion processes revisited , 2013 .
[17] Evgueni A. Haroutunian,et al. Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.
[18] S. Schweitzer. A Course In Differential Geometry , 2016 .
[19] Gersende Fort,et al. On Perturbed Proximal Gradient Algorithms , 2014, J. Mach. Learn. Res..
[20] Eric Moulines,et al. Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau , 2016, SIAM J. Imaging Sci..
[21] Jon Wakefield,et al. Bayesian and Frequentist Regression Methods , 2013 .
[22] G. Fort,et al. Convergence of adaptive and interacting Markov chain Monte Carlo algorithms , 2011, 1203.3036.
[23] D. Vere-Jones. Markov Chains , 1972, Nature.
[24] C. Andrieu,et al. On the ergodicity properties of some adaptive MCMC algorithms , 2006, math/0610317.
[25] A. Dalalyan. Theoretical guarantees for approximate sampling from smooth and log‐concave densities , 2014, 1412.7392.
[26] Mário A. T. Figueiredo,et al. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.
[27] Mathews Jacob,et al. A blind compressive sensing frame work for accelerated dynamic MRI , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).
[28] M. Girolami,et al. Riemann manifold Langevin and Hamiltonian Monte Carlo methods , 2011, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[29] Christian P. Robert,et al. Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.
[30] Alain Durmus,et al. Convergence of diffusions and their discretizations: from continuous to discrete processes and back , 2019, 1904.09808.
[31] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[32] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[33] É. Moulines,et al. On the convergence of Hamiltonian Monte Carlo , 2017, 1705.00166.
[34] Pierre Priouret,et al. Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.
[35] R. Tweedie,et al. Exponential convergence of Langevin distributions and their discrete approximations , 1996 .
[36] David Williams,et al. Probability with Martingales , 1991, Cambridge mathematical textbooks.
[37] A. Eberle,et al. Couplings and quantitative contraction rates for Langevin dynamics , 2017, The Annals of Probability.
[38] M. Yor,et al. Continuous martingales and Brownian motion , 1990 .
[39] O. Kallenberg. Foundations of Modern Probability , 2021, Probability Theory and Stochastic Modelling.
[40] S. F. Jarner,et al. Geometric ergodicity of Metropolis algorithms , 2000 .
[41] G. Casella. An Introduction to Empirical Bayes Data Analysis , 1985 .
[42] H. Robbins. A Stochastic Approximation Method , 1951 .
[43] Mateusz B. Majka,et al. Quantitative contraction rates for Markov chains on general state spaces , 2018, Electronic Journal of Probability.
[44] S. Meyn,et al. Stability of Markovian processes III: Foster–Lyapunov criteria for continuous-time processes , 1993, Advances in Applied Probability.
[45] Yuichi Mori,et al. Handbook of computational statistics : concepts and methods , 2004 .
[46] D. W. Stroock,et al. Multidimensional Diffusion Processes , 1979 .
[47] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.
[48] Dimitri P. Bertsekas,et al. Nonlinear Programming , 1997 .
[49] Yuichi Mori,et al. Handbook of Computational Statistics , 2004 .
[50] James G. Scott,et al. Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables , 2012, 1205.0310.
[51] T. Louis,et al. Empirical Bayes: Past, Present and Future , 2000 .
[52] É. Moulines,et al. Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm , 2015, 1507.05021.
[53] V. V. Buldygin,et al. Brunn-Minkowski inequality , 2000 .
[54] C. Robert,et al. Computational methods for Bayesian model choice , 2009, 0907.5123.
[55] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[56] Marcelo Pereyra,et al. Maximum Likelihood Estimation of Regularisation Parameters , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).