Research map / Optimization / Nonconvex and smooth optimization
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations
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
- Stochastic Gradient Methods under Heavy-Tailed Noises in Weakly Convex Optimization (ICML 2026 · similarity 0.94)
- Error Analysis Affected by Heavy-Tailed Gradients for Non-Convex Pairwise Stochastic Gradient Descent (AAAI 2025 · similarity 0.92)
- High-probability complexity bounds for stochastic non-convex minimax optimization (NeurIPS 2024 · similarity 0.91)
- Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits (NeurIPS 2025 · similarity 0.91)
- Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise (NeurIPS 2026 · similarity 0.90)
- Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems (NeurIPS 2026 · similarity 0.90)
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.
