proximal operator

A Generic Acceleration Framework for Stochastic Composite Optimization NeurIPS 2019 Differentiable Linearized ADMM ICML 2019 Douglas-Rachford Networks: Learning Both the Image Prior and Data Fidelity Terms for Blind Image Deconvolution CVPR 2019 Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization AAAI 2019 Lifted Proximal Operator Machines AAAI 2019 Proximal Splitting Meets Variance Reduction AISTATS 2019 ProxQuant: Quantized Neural Networks via Proximal Operators ICLR 2019 Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NeurIPS 2019 Adaptive Three Operator Splitting ICML 2018 Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice JMLR 2018 Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach AISTATS 2018 Efficient Projection onto the Perfect Phylogeny Model NeurIPS 2018 Faster Training Algorithms for Structured Sparsity-Inducing Norm IJCAI 2018 Proximal Backpropagation ICLR 2018 Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization NeurIPS 2017 Identifying Groups of Strongly Correlated Variables through Smoothed Ordered Weighted L_1-norms AISTATS 2017 Epigraph projections for fast general convex programming ICML 2016 Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization ACML 2016 New Perspectives on k-Support and Cluster Norms JMLR 2016 Stochastic Three-Composite Convex Minimization NeurIPS 2016 Composite Self-Concordant Minimization JMLR 2015 Generalized Dantzig Selector: Application to the k-support norm NeurIPS 2014 SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives NeurIPS 2014 A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems ICML 2013 On Decomposing the Proximal Map NeurIPS 2013