Optimization with Sparsity-Inducing Penalties, Machine Learning, pp.1-106, 2012. ,
DOI : 10.1561/2200000015
URL : https://hal.archives-ouvertes.fr/hal-00613125
Structured Sparsity through Convex Optimization, Statistical Science, vol.27, issue.4, pp.450-468, 2012. ,
DOI : 10.1214/12-STS394
URL : https://hal.archives-ouvertes.fr/hal-00621245
Adaptive regression and model selection in data mining problems, p.22, 1999. ,
Convex analysis and monotone operator theory in Hilbert spaces, p.24, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-01517477
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009. ,
DOI : 10.1137/080716542
A quasi-newton proximal splitting method, Advances in Neural Information Processing Systems, pp.2618-2626, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-01080081
Nonlinear programming. Athena scientific, p.27, 1999. ,
Convex optimization algorithms, Athena Scientific Belmont, p.26, 2015. ,
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, pp.1-122, 2011. ,
DOI : 10.1561/2200000016
Subgradient methods. lecture notes of EE392o, pp.2004-2005, 2003. ,
Interactive graph cuts for optimal boundary & region segmentation of objects in nd images, Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, pp.105-112, 2001. ,
Quasi-Newton methods and their application to function minimisation, Mathematics of Computation, vol.21, issue.99, pp.368-381, 1967. ,
DOI : 10.1090/S0025-5718-1967-0224273-2
A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, pp.120-145, 2011. ,
DOI : 10.1007/978-3-540-74936-3_22
URL : https://hal.archives-ouvertes.fr/hal-00490826
Nested Iterative Algorithms for Convex Constrained Image Recovery Problems, SIAM Journal on Imaging Sciences, vol.2, issue.2, pp.730-762, 2009. ,
DOI : 10.1137/080727749
URL : https://hal.archives-ouvertes.fr/hal-00621932
Convergence Rates in Forward--Backward Splitting, SIAM Journal on Optimization, vol.7, issue.2, pp.421-444, 1997. ,
DOI : 10.1137/S1052623495290179
Solving monotone inclusions via compositions of nonexpansive averaged operators. Optimization, pp.475-504, 2004. ,
URL : https://hal.archives-ouvertes.fr/hal-00017830
A proximal decomposition method for solving convex variational inverse problems, Inverse Problems, vol.24, issue.6, pp.65014-65042, 2008. ,
DOI : 10.1088/0266-5611/24/6/065014
URL : https://hal.archives-ouvertes.fr/hal-00692901
Proximal Splitting Methods in Signal Processing, 2009. ,
DOI : 10.1007/978-1-4419-9569-8_10
URL : https://hal.archives-ouvertes.fr/hal-00643807
Stochastic Quasi-Fej??r Block-Coordinate Fixed Point Iterations with Random Sweeping, SIAM Journal on Optimization, vol.25, issue.2, pp.1221-1248, 2015. ,
DOI : 10.1137/140971233
URL : http://arxiv.org/pdf/1404.7536
Variable metric forward???backward splitting with applications to monotone inclusions in duality, Optimization, vol.76, issue.9, pp.1289-1318, 2014. ,
DOI : 10.1137/S1052623494250415
URL : https://hal.archives-ouvertes.fr/hal-01158997
Signal Recovery by Proximal Forward-Backward Splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005. ,
DOI : 10.1137/050626090
URL : https://hal.archives-ouvertes.fr/hal-00017649
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.1413-1457, 2004. ,
DOI : 10.1002/0471221317
Variable Metric Method for Minimization, SIAM Journal on Optimization, vol.1, issue.1, pp.1-17, 1991. ,
DOI : 10.1137/0801001
On the numerical solution of heat conduction problems in two and three space variables, Transactions of the American Mathematical Society, vol.82, issue.2, pp.421-439, 1956. ,
DOI : 10.1090/S0002-9947-1956-0084194-4
General Projective Splitting Methods for Sums of Maximal Monotone Operators, SIAM Journal on Control and Optimization, vol.48, issue.2, pp.787-811, 2009. ,
DOI : 10.1137/070698816
A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science, SIAM Journal on Imaging Sciences, vol.3, issue.4, pp.1015-1046, 2010. ,
DOI : 10.1137/09076934X
Diagonal scaling in Douglas-Rachford splitting and ADMM, 53rd IEEE Conference on Decision and Control, pp.5033-5039, 2014. ,
DOI : 10.1109/CDC.2014.7040175
Metric selection in fast dual forward???backward splitting, Automatica, vol.62, p.33, 2014. ,
DOI : 10.1016/j.automatica.2015.09.010
Parametric Maximum Flow Algorithms for Fast Total Variation Minimization, SIAM Journal on Scientific Computing, vol.31, issue.5, pp.313712-3743, 2009. ,
DOI : 10.1137/070706318
Sur les problèmes aux dérivées partielles et leur signification physique, Princeton university bulletin, vol.13, pp.49-5228, 1902. ,
Learning with structured sparsity, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.3371-3412, 2011. ,
DOI : 10.1145/1553374.1553429
Two remarks on the method of successive approximations, pp.123-127, 1955. ,
Convergence rates with inexact non-expansive operators, Mathematical Programming, pp.1-32, 2014. ,
DOI : 10.1007/s10444-011-9254-8
URL : https://hal.archives-ouvertes.fr/hal-01658852
An Inertial Forward-Backward Algorithm for Monotone Inclusions, Journal of Mathematical Imaging and Vision, vol.23, issue.3, pp.311-325, 2014. ,
DOI : 10.1137/110844805
Mean value methods in iteration, Proceedings of the American Mathematical Society, pp.506-510, 1953. ,
DOI : 10.1090/S0002-9939-1953-0054846-3
Proximit?? et dualit?? dans un espace hilbertien, Bulletin de la Société mathématique de France, vol.79, pp.273-299, 1965. ,
DOI : 10.24033/bsmf.1625
Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.577-685, 1989. ,
DOI : 10.1109/TPAMI.1984.4767596
A method of solving a convex programming problem with convergence rate o(1/k2), Soviet Mathematics Doklady, vol.27, issue.2, pp.372-376, 1983. ,
Introductory lectures on convex optimization, p.30, 2004. ,
DOI : 10.1007/978-1-4419-8853-9
Gradient methods for minimizing composite functions, Mathematical Programming, pp.125-161, 2013. ,
DOI : 10.1109/TIT.2005.864420
Group lasso with overlaps: the latent group lasso approach. arXiv preprint, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00628498
A Class of Inexact Variable Metric Proximal Point Algorithms, SIAM Journal on Optimization, vol.19, issue.1, pp.240-260, 2008. ,
DOI : 10.1137/070688146
Proximal Algorithms, Foundations and Trends?? in Optimization, vol.1, issue.3, pp.123-231, 2013. ,
DOI : 10.1561/2400000003
Ergodic convergence to a zero of the sum of monotone operators in Hilbert space, Journal of Mathematical Analysis and Applications, vol.72, issue.2, pp.383-390, 1979. ,
DOI : 10.1016/0022-247X(79)90234-8
Non-local Regularization of Inverse Problems, Computer Vision?ECCV 2008, pp.57-68, 2008. ,
DOI : 10.1109/TIT.2006.871582
Diagonal preconditioning for first order primaldual algorithms in convex optimization, Computer Vision (ICCV), 2011 IEEE International Conference on, pp.1762-1769, 2011. ,
A modification of the Arrow-Hurwicz method for search of saddle points, Mathematical Notes of the Academy of Sciences of the USSR, vol.13, issue.No. 2, pp.845-848, 1980. ,
DOI : 10.1007/BF01141092
Variable metric proximal point algorithm: convergence theory and applications, p.31, 1992. ,
A Generalized Forward-Backward Splitting, SIAM Journal on Imaging Sciences, vol.6, issue.3, pp.1199-1226, 2013. ,
DOI : 10.1137/120872802
URL : https://hal.archives-ouvertes.fr/hal-00613637
Preconditioning of a Generalized Forward-Backward Splitting and Application to Optimization on Graphs, SIAM Journal on Imaging Sciences, vol.8, issue.4, p.35, 2015. ,
DOI : 10.1137/15M1018253
URL : https://hal.archives-ouvertes.fr/hal-01144566
Convergence of Krasnoselskii-Mann iterations of nonexpansive operators, Mathematical and Computer Modelling, vol.32, issue.11-13, pp.1423-1431, 2000. ,
DOI : 10.1016/S0895-7177(00)00214-4
A random block-coordinate primal-dual proximal algorithm with application to 3D mesh denoising, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3561-3565, 2015. ,
DOI : 10.1109/ICASSP.2015.7178634
URL : https://hal.archives-ouvertes.fr/hal-01370504
Sur une espèce de géométrie analytique des systèmes de fonctions sommables, CR Acad. Sci. Paris, vol.144, pp.1409-1411, 1907. ,
Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.1-4259, 1992. ,
DOI : 10.1016/0167-2789(92)90242-F
Partial inverse of a monotone operator Applied mathematics and optimization, pp.247-265, 1983. ,
Image Segmentation Using Active Contours: Calculus of Variations or Shape Gradients?, SIAM Journal on Applied Mathematics, vol.63, issue.6, pp.2128-2154, 2003. ,
DOI : 10.1137/S0036139902408928
URL : https://hal.archives-ouvertes.fr/inria-00072105
Learning with Submodular Functions: A Convex Optimization Perspective, Foundations and Trends?? in Machine Learning, vol.6, issue.2-3, pp.145-373, 2013. ,
DOI : 10.1561/2200000039
URL : https://hal.archives-ouvertes.fr/hal-00645271
Optimization with Sparsity-Inducing Penalties, Machine Learning, pp.1-106, 2012. ,
DOI : 10.1561/2200000015
URL : https://hal.archives-ouvertes.fr/hal-00613125
Shaping level sets with submodular functions, Advances in Neural Information Processing Systems, pp.10-18, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00542949
A note on cluster analysis and dynamic programming, Mathematical Biosciences, vol.18, issue.3-4, pp.311-312, 1973. ,
DOI : 10.1016/0025-5564(73)90007-2
The group fused Lasso for multiple change-point detection. arXiv preprint, p.74, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00602121
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004. ,
DOI : 10.1109/TPAMI.2004.60
Efficient approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.12, pp.1222-1239, 2001. ,
Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001. ,
DOI : 10.1109/34.969114
Fast Global Minimization of the Active Contour/Snake Model, Journal of Mathematical Imaging and Vision, vol.7, issue.3, pp.151-167, 2007. ,
DOI : 10.1007/b98879
An introduction to total variation for image analysis, Theoretical foundations and numerical methods for sparse recovery, pp.263-340, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00437581
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows, International Journal of Computer Vision, vol.40, issue.9, pp.288-307, 2009. ,
DOI : 10.1006/jctb.2000.1989
A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, pp.120-145, 2011. ,
DOI : 10.1007/978-3-540-74936-3_22
URL : https://hal.archives-ouvertes.fr/hal-00490826
Total Variation Image Restoration: Overview and Recent Developments, Mathematical Models of Computer Vision, pp.17-31, 2005. ,
DOI : 10.1007/0-387-28831-7_2
Active contours without edges, IEEE Transactions on Image Processing, vol.10, issue.2, pp.266-277, 2001. ,
DOI : 10.1109/83.902291
The Convex Geometry of Linear Inverse Problems, Foundations of Computational Mathematics, vol.1, issue.10, pp.805-849, 2012. ,
DOI : 10.1007/978-1-4613-8431-1
Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, vol.2, issue.2, pp.302-309, 1991. ,
DOI : 10.1109/72.80341
On Nonlinear Fractional Programming, Management Science, vol.13, issue.7, pp.492-498, 1967. ,
DOI : 10.1287/mnsc.13.7.492
Least angle regression. The Annals of statistics, pp.407-499, 2004. ,
Multiple regression analysis Mathematical methods for digital computers, pp.191-203, 1960. ,
Discrete Optimization of the Multiphase Piecewise Constant Mumford-Shah Functional, Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.233-246, 2011. ,
DOI : 10.1109/42.712135
Combinatorial Optimization of the piecewise constant Mumford-Shah functional with application to scalar/vector valued and volumetric image segmentation, Image and Vision Computing, vol.29, issue.6, pp.29365-381, 2011. ,
DOI : 10.1016/j.imavis.2010.09.002
Brain MRI tissue classification using graph cut optimization of the Mumford?Shah functional, Proceedings of the International Vision Conference of New Zealand, pp.321-326, 2007. ,
Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010. ,
DOI : 10.18637/jss.v033.i01
Class segmentation and object localization with superpixel neighborhoods, 2009 IEEE 12th International Conference on Computer Vision, pp.670-677, 2009. ,
DOI : 10.1109/ICCV.2009.5459175
Constrained restoration and the recovery of discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.3, pp.367-383, 1992. ,
DOI : 10.1109/34.120331
Parametric Maximum Flow Algorithms for Fast Total Variation Minimization, SIAM Journal on Scientific Computing, vol.31, issue.5, pp.3712-3743, 2009. ,
DOI : 10.1137/070706318
Conditional gradient algorithms for norm-regularized smooth convex optimization, Mathematical Programming, vol.82, issue.281, pp.60-83, 2014. ,
DOI : 10.1090/S0025-5718-2012-02598-1
URL : https://hal.archives-ouvertes.fr/hal-00978368
Exact optimization for markov random fields with convex priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.10, pp.1333-1336, 2003. ,
DOI : 10.1109/TPAMI.2003.1233908
Revisiting Frank-Wolfe: projection-free sparse convex optimization, Proceedings of the 30th International Conference on Machine Learning, pp.427-435, 2013. ,
Reflection methods for user-friendly submodular optimization, Advances in Neural Information Processing Systems, pp.1313-1321, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00905258
Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-331, 1988. ,
DOI : 10.1007/BF00133570
Efficiently solving dynamic Markov random fields using graph cuts, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.922-929, 2005. ,
DOI : 10.1109/ICCV.2005.81
What energy functions can be minimized via graph cuts?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.147-159, 2004. ,
DOI : 10.1109/TPAMI.2004.1262177
Maximum likelihood detection and estimation of Bernoulli - Gaussian processes, IEEE Transactions on Information Theory, vol.28, issue.3, pp.482-488, 1982. ,
DOI : 10.1109/TIT.1982.1056496
Active-set methods for submodular optimization. arXiv preprint, p.64, 2015. ,
Cut pursuit: fast algorithms to learn piecewise constant functions, 19th International Conference on Artificial Intelligence and Statistics, p.59, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01306786
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions on General Weighted Graphs, SIAM Journal on Imaging Sciences, vol.10, issue.4, p.59, 2016. ,
DOI : 10.1137/17M1113436
URL : https://hal.archives-ouvertes.fr/hal-01306779
An Inertial Forward-Backward Algorithm for Monotone Inclusions, Journal of Mathematical Imaging and Vision, vol.23, issue.3, pp.311-325, 2014. ,
DOI : 10.1137/110844805
Adaptive time-frequency decomposition with matching pursuits. In Time-Frequency and Time-Scale Analysis, Proceedings of the IEEE-SP International Symposium, pp.7-10, 1992. ,
DOI : 10.1109/tftsa.1992.274245
Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.577-685, 1989. ,
DOI : 10.1109/TPAMI.1984.4767596
URL : https://dash.harvard.edu/bitstream/handle/1/3637121/Mumford_OptimalApproxPiece.pdf?sequence=1
CoSaMP, Communications of the ACM, vol.53, issue.12, pp.301-321, 2009. ,
DOI : 10.1145/1859204.1859229
A Unified Framework for High-Dimensional Analysis of $M$-Estimators with Decomposable Regularizers, Advances in Neural Information Processing Systems, pp.1348-1356, 2009. ,
DOI : 10.1214/12-STS400SUPP
Gradient methods for minimizing composite objective function, Center for Operations Research and Econometrics, p.83, 2007. ,
Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning, IEEE Signal Processing Letters, vol.21, issue.10, pp.1289-1292, 2014. ,
DOI : 10.1109/LSP.2014.2333001
Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction, IEEE Transactions on Image Processing, vol.19, issue.12, pp.3073-3088, 2010. ,
DOI : 10.1109/TIP.2010.2052275
Multi-task feature selection, Statistics Department, p.74, 2006. ,
On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision, SIAM Journal on Imaging Sciences, vol.8, issue.1, pp.331-372, 2015. ,
DOI : 10.1137/140971518
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol.79, issue.1, pp.12-49, 1988. ,
DOI : 10.1016/0021-9991(88)90002-2
Minimum cuts and related problems, Networks, vol.17, issue.4, pp.357-370, 1975. ,
DOI : 10.1007/978-3-642-85823-9
Diagonal preconditioning for first order primal-dual algorithms in convex optimization, 2011 International Conference on Computer Vision, pp.1762-1769, 2011. ,
DOI : 10.1109/ICCV.2011.6126441
A Generalized Forward-Backward Splitting, SIAM Journal on Imaging Sciences, vol.6, issue.3, pp.1199-1226, 2013. ,
DOI : 10.1137/120872802
URL : https://hal.archives-ouvertes.fr/hal-00613637
Preconditioning of a Generalized Forward-Backward Splitting and Application to Optimization on Graphs, SIAM Journal on Imaging Sciences, vol.8, issue.4, pp.2706-2739, 2015. ,
DOI : 10.1137/15M1018253
URL : https://hal.archives-ouvertes.fr/hal-01144566
Convex analysis, p.68, 1970. ,
DOI : 10.1515/9781400873173
The Group-Lasso for generalized linear models, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.848-855, 2008. ,
DOI : 10.1145/1390156.1390263
Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992. ,
DOI : 10.1016/0167-2789(92)90242-F
Interactive Multi-label Segmentation, pp.397-410, 2011. ,
DOI : 10.2307/1932409
The Fourier reconstruction of a head section, IEEE Transactions on Nuclear Science, vol.21, issue.3, pp.21-43, 1974. ,
DOI : 10.1109/TNS.1974.6499235
From Bernoulli???Gaussian Deconvolution to Sparse Signal Restoration, IEEE Transactions on Signal Processing, vol.59, issue.10, pp.4572-4584, 2011. ,
DOI : 10.1109/TSP.2011.2160633
URL : https://hal.archives-ouvertes.fr/hal-00443842
A Comparative Study of Energy Minimization Methods for Markov Random Fields, Proceeding of the European Conference in Computer Vision (ECCV), pp.16-29, 2006. ,
DOI : 10.1109/ICCV.2005.14
Total variation and level set methods in image science, Acta Numerica, vol.14, pp.509-573, 2005. ,
DOI : 10.1017/S0962492904000273
A multiphase level set framework for image segmentation using the Mumford and Shah model, International Journal of Computer Vision, vol.50, issue.3, pp.271-293, 2002. ,
DOI : 10.1023/A:1020874308076
Trend filtering on graphs, p.60, 2014. ,
Adaptive forward-backward greedy algorithm for sparse learning with linear models, Advances in Neural Information Processing Systems, pp.1921-1928, 2009. ,
The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006. ,
DOI : 10.1198/016214506000000735
Continuous time markov chain models for chemical reaction networks, Design and analysis of biomolecular circuits, pp.3-42, 2011. ,
Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images, IEEE Transactions on pattern analysis and machine intelligence, issue.6, pp.721-741, 1984. ,
Parallel Gibbs sampling: From colored fields to thin junction trees, International Conference on Artificial Intelligence and Statistics, pp.324-332, 2011. ,
Fundamentals of queueing theory, p.111, 1998. ,
DOI : 10.1002/9781118625651
Markov fields on finite graphs and lattices, 1971. ,
Stable fixed points of loopy belief propagation are local minima of the Bethe free energy, Advances in neural information processing systems, pp.343-350, 2002. ,
On the Uniqueness of Loopy Belief Propagation Fixed Points, Neural Computation, vol.50, issue.11, pp.2379-2413, 2004. ,
DOI : 10.1162/08997660260028674
An expectation maximization algorithm for training hidden substitution models 1 1Edited by F. Cohen, Journal of Molecular Biology, vol.317, issue.5, pp.753-764, 2002. ,
DOI : 10.1006/jmbi.2002.5405
Loopy belief propagation: Convergence and effects of message errors, The Journal of Machine Learning Research, vol.6, pp.905-936, 2005. ,
Reproduction numbers and thresholds in stochastic epidemic models I. Homogeneous populations, Mathematical Biosciences, vol.107, issue.2, pp.161-186, 1991. ,
DOI : 10.1016/0025-5564(91)90003-2
Foundations of modern probability, p.112, 2006. ,
DOI : 10.1007/978-1-4757-4015-8
On methods for studying stochastic disease dynamics, Journal of The Royal Society Interface, vol.116, issue.3, pp.171-181, 2008. ,
DOI : 10.1017/S0950268800052699
Conditional random fields: Probabilistic models for segmenting and labeling sequence data, 2001. ,
Stochastic belief propagation: A lowcomplexity alternative to the sum-product algorithm. Information Theory, IEEE Transactions on, vol.59, issue.4, pp.1981-2000, 2013. ,
Loopy belief propagation and gibbs measures, Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence, pp.493-500, 2002. ,
Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends?? in Machine Learning, vol.1, issue.1???2, pp.1-305, 2008. ,
DOI : 10.1561/2200000001
URL : http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf
Correctness of Local Probability Propagation in Graphical Models with Loops, Neural Computation, vol.12, issue.1, pp.1-41, 2000. ,
DOI : 10.1162/neco.1997.9.2.227
Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms, IEEE Transactions on Information Theory, vol.51, issue.7, pp.2282-2312, 2005. ,
DOI : 10.1109/TIT.2005.850085
Figure 6 Quantized levels of the random Gaussian map. (middle) nodes drawn from the map with nodes whose labels are provided to the algorithm circled in black. (bottom) predictions of the CGMRF with mistakes marked with ×. 6.4 Experiments Bibliography Bach Learning spectral clustering, with application to speech separation, The Journal of Machine Learning Research, vol.4, issue.7, pp.1963-2001, 2006. ,
Introduction to Lambda Trees, p.125, 2001. ,
Stochastic Processes, Estimation, and Control, p.125, 1998. ,
Probability models for DNA sequence evolution, p.121, 2008. ,
Prediction on a graph with a perceptron, Advances in Neural Information Processing Systems, pp.577-584, 2006. ,
An expectation maximization algorithm for training hidden substitution models 1 1Edited by F. Cohen, Journal of Molecular Biology, vol.317, issue.5, pp.753-764, 2002. ,
DOI : 10.1006/jmbi.2002.5405
Continuously indexed potts models on unoriented graphs, UAI 20114-30th Conference on Uncertainty in Artificial Intelligence, pp.459-468, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01060957
Statistical methods in molecular evolution, p.121, 2005. ,
DOI : 10.1007/0-387-27733-1
Numerical Optimization, p.132, 1999. ,
DOI : 10.1007/b98874
Continuous time Bayesian networks, Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence, pp.378-387, 2002. ,
Markov chains, p.121, 1997. ,
DOI : 10.1017/CBO9780511810633
A matrix handbook for statisticians, p.124, 2008. ,
DOI : 10.1002/9780470226797
Modeling DNA base substitution in large genomic regions from two organisms, Journal of Molecular Evolution, vol.58, issue.1, pp.12-18, 2004. ,
Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends?? in Machine Learning, vol.1, issue.1???2, pp.1-305, 2008. ,
DOI : 10.1561/2200000001
A continuum generalization of the Ising model. arXiv1306, p.121, 2013. ,
Semi-supervised learning using Gaussian fields and harmonic functions, Proceedings of the International Conference on Machine Learning (ICML), pp.912-919, 2003. ,
Introduction to Semi-Supervised Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.35, issue.8, pp.1-130, 2009. ,
DOI : 10.1109/TKDE.2005.186
Proximity-based grouping of buildings in urban blocks: a comparison of four algorithms, Geocarto International, vol.30, issue.6, pp.618-632, 2015. ,
DOI : 10.1080/13658816.2012.758264
Parametric Maximum Flow Algorithms for Fast Total Variation Minimization, SIAM Journal on Scientific Computing, vol.31, issue.5, pp.313712-3743, 2009. ,
DOI : 10.1137/070706318
Relative neighborhood graphs and their relatives, Proceedings of the IEEE, pp.1502-1517, 1992. ,
DOI : 10.1109/5.163414
Neighborhood Planning, Journal of Planning Education and Research, vol.60, issue.11, pp.111-114, 2000. ,
DOI : 10.1080/01944368508976207
Augmented Lagrangian and alternating direction methods for convex optimization: A tutorial and some illustrative computational results, p.160, 2012. ,
Learning with Submodular Functions: A Convex Optimization Perspective, Foundations and Trends?? in Machine Learning, vol.6, issue.2-3, pp.145-373, 2013. ,
DOI : 10.1561/2200000039
URL : https://hal.archives-ouvertes.fr/hal-00645271
Shaping level sets with submodular functions, Advances in Neural Information Processing Systems, pp.10-18, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00542949
The Convex Geometry of Linear Inverse Problems, Foundations of Computational Mathematics, vol.1, issue.10, pp.805-849, 2012. ,
DOI : 10.1007/978-1-4613-8431-1
On Nonlinear Fractional Programming, Management Science, vol.13, issue.7, pp.492-498, 1967. ,
DOI : 10.1287/mnsc.13.7.492
Revisiting Frank-Wolfe: projection-free sparse convex optimization, Proceedings of the 30th International Conference on Machine Learning, pp.427-435, 2013. ,
Forward???Backward Greedy Algorithms for Atomic Norm Regularization, IEEE Transactions on Signal Processing, vol.63, issue.21, pp.5798-5811, 2015. ,
DOI : 10.1109/TSP.2015.2461515
Convex analysis, p.163, 1970. ,
DOI : 10.1515/9781400873173