Research map / Optimization / Nonconvex and smooth optimization
Never Go Full Batch (in Stochastic Convex 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
- The Sample Complexity of Gradient Descent in Stochastic Convex Optimization (NeurIPS 2024 · similarity 0.94)
- Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization (ICML 2019 · similarity 0.91)
- Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD (ICLR 2023 · similarity 0.91)
- Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization (NeurIPS 2021 · similarity 0.90)
- Universal Gradient Methods for Stochastic Convex Optimization (ICML 2024 · similarity 0.90)
- Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations (ICML 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.