proximal algorithm

Langevin Monte Carlo Beyond Lipschitz Gradient Continuity AAAI 2025 Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method JMLR 2025 Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient ICML 2024 Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression NeurIPS 2024 Fitting ARMA Time Series Models without Identification: A Proximal Approach AISTATS 2024 From Compliant to Rigid Contact Simulation: a Unified and Efficient Approach RSS 2024 Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions JMLR 2024 Structured Transforms Across Spaces with Cost-Regularized Optimal Transport AISTATS 2024 A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization UAI 2023 Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems NeurIPS 2023 Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework ICML 2023 Extensions to the Proximal Distance Method of Constrained Optimization JMLR 2022 Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization ICML 2022 TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems JMLR 2022 Factorization Machines with Regularization for Sparse Feature Interactions JMLR 2021 Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods NeurIPS 2021 Stochastic Proximal AUC Maximization JMLR 2021 A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems JMLR 2020 A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent AISTATS 2020 Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis JMLR 2020 Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning AISTATS 2020 ProxSGD: Training Structured Neural Networks under Regularization and Constraints ICLR 2020 Support recovery and sup-norm convergence rates for sparse pivotal estimation AISTATS 2020 Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems ICML 2020 A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression NeurIPS 2019