Research map / Optimization / Optimizers
Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks (ICML 2025 · similarity 0.89)
- Topology-aware Generalization of Decentralized SGD (ICML 2022 · similarity 0.87)
- Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization (NeurIPS 2022 · similarity 0.87)
- Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence (NeurIPS 2023 · similarity 0.87)
- Improved Convergence Analysis of Topology Dependence in Decentralized SGD (ICML 2026 · similarity 0.86)
- Stability and Generalization of Zeroth-Order Decentralized Stochastic Gradient Descent with Changing Topology (AAAI 2025 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
