proximal operator

Decoupling Training-Free Guided Diffusion by ADMM CVPR 2025 Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization ICML 2025 OTPNet: ODE-inspired Tuning-free Proximal Network for Remote Sensing Image Fusion AAAI 2025 A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity JMLR 2024 Learning Elastic Costs to Shape Monge Displacements NeurIPS 2024 Limited-Supervised Multi-Label Learning with Dependency Noise AAAI 2024 What's in a Prior? Learned Proximal Networks for Inverse Problems ICLR 2024 Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems NeurIPS 2023 Empowering Low-Light Image Enhancer through Customized Learnable Priors ICCV 2023 Fast and Interpretable Dynamics for Fisher Markets via Block-Coordinate Updates AAAI 2023 Learning Proximal Operators to Discover Multiple Optima ICLR 2023 Tighter Analysis for ProxSkip ICML 2023 Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers NeurIPS 2023 An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022 Convergence Analysis of Linear Coupling with Inexact Proximal Operator UAI 2022 Proximal and Federated Random Reshuffling ICML 2022 Proximal Learning With Opponent-Learning Awareness NeurIPS 2022 On Characterizing GAN Convergence Through Proximal Duality Gap ICML 2021 Three Operator Splitting with a Nonconvex Loss Function ICML 2021 A principled approach for generating adversarial images under non-smooth dissimilarity metrics AISTATS 2020 A Unified Optimization Framework for Low-Rank Inducing Penalties CVPR 2020 Efficient Proximal Mapping of the 1-path-norm of Shallow Networks ICML 2020 Learning Interpretable Models using Soft Integrity Constraints ACML 2020 Learning With Subquadratic Regularization : A Primal-Dual Approach IJCAI 2020 Adaptive Proximal Average Based Variance Reducing Stochastic Methods for Optimization with Composite Regularization AAAI 2019