Research map / Optimization / Nonconvex and smooth optimization
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach
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
- First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization (NeurIPS 2017 · similarity 0.93)
- Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy (NeurIPS 2016 · similarity 0.92)
- Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values (ICML 2017 · similarity 0.90)
- Approximate Newton Methods and Their Local Convergence (ICML 2017 · similarity 0.89)
- Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes (ICML 2025 · similarity 0.86)
- On the Asymptotic Distribution of the Minimum Empirical Risk (ICML 2024 · similarity 0.86)
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.
