Research map / Optimization / Nonconvex and smooth optimization
Fast convergence of stochastic subgradient method under interpolation
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
- On the Convergence of mSGD and AdaGrad for Stochastic Optimization (ICLR 2022 · similarity 0.91)
- Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime (NeurIPS 2025 · similarity 0.91)
- Benign Underfitting of Stochastic Gradient Descent (NeurIPS 2022 · similarity 0.90)
- The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning (ICML 2018 · similarity 0.90)
- Derivatives of Stochastic Gradient Descent in parametric optimization (NeurIPS 2024 · similarity 0.90)
- Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods (ICLR 2024 · similarity 0.89)
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.
