gradient tracking

Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex Optimization JMLR 2026 Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks ICML 2025 A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds JMLR 2025 Decentralized Sparse Linear Regression via Gradient-Tracking JMLR 2025 Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis JMLR 2025 DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity ICLR 2025 Scalability Enhancement and Data-Heterogeneity Awareness in Gradient Tracking based Decentralized Bayesian Learning L4DC 2025 Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization JMLR 2024 Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning JMLR 2024 Decentralized Sum-of-Nonconvex Optimization AAAI 2024 SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization NeurIPS 2024 Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data ICML 2023 On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network AISTATS 2023 Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function ICML 2023 Acceleration in Distributed Sparse Regression NeurIPS 2022 A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning NeurIPS 2022 A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms ICML 2022 BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression NeurIPS 2022 On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces ICML 2022 Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology ICML 2022 An Improved Analysis of Gradient Tracking for Decentralized Machine Learning NeurIPS 2021 Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction ICML 2021 Newton Method over Networks is Fast up to the Statistical Precision ICML 2021 Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction AISTATS 2020 Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction JMLR 2020