Research map / Optimization / Nonconvex and smooth optimization
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization
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 Bias-Reduced Gradient Methods (NeurIPS 2021 · similarity 0.91)
- Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences (NeurIPS 2016 · similarity 0.90)
- Distributionally Robust Optimization with Bias and Variance Reduction (ICLR 2024 · similarity 0.89)
- Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems (NeurIPS 2026 · similarity 0.88)
- Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization (NeurIPS 2025 · similarity 0.88)
- Conditional Gradient Methods with Standard LMO for Stochastic Simple Bilevel Optimization (NeurIPS 2025 · similarity 0.87)
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.