proximal mapping

S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems AAAI 2026 Proximal Mapping Loss: Understanding Loss Functions in Crowd Counting & Localization ICLR 2025 An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems JMLR 2024 Weight-Sharing Regularization AISTATS 2024 Lifted Bregman Training of Neural Networks JMLR 2023 HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging CVPR 2022 On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping JMLR 2022 Demystifying and Generalizing BinaryConnect NeurIPS 2021 Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization NeurIPS 2021 One-sided Frank-Wolfe algorithms for saddle problems ICML 2021 Halpern Iteration for Near-Optimal and Parameter-Free Monotone Inclusion and Strong Solutions to Variational Inequalities COLT 2020 Proximal Mapping for Deep Regularization NeurIPS 2020 An Optimal High-Order Tensor Method for Convex Optimization COLT 2019 Optimization of Inf-Convolution Regularized Nonconvex Composite Problems AISTATS 2019 SpiderBoost and Momentum: Faster Variance Reduction Algorithms NeurIPS 2019 Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence ICML 2019 Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization ACML 2016 On Decomposing the Proximal Map NeurIPS 2013 Optimal Regularized Dual Averaging Methods for Stochastic Optimization NeurIPS 2012