Research map / Optimization / Nonconvex and smooth optimization
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
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
- Variance Reduction for Faster Non-Convex Optimization (ICML 2016 · similarity 0.89)
- Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction (NeurIPS 2024 · similarity 0.89)
- Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure (NeurIPS 2017 · similarity 0.89)
- Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs (ICML 2018 · similarity 0.88)
- Efficient Continual Finite-Sum Minimization (ICLR 2024 · similarity 0.88)
- Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness (ICLR 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.