Research map / Optimization / Implicit bias and generalization
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent
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
- Emergence of heavy tails in homogenized stochastic gradient descent (NeurIPS 2024 · similarity 0.92)
- Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD (NeurIPS 2024 · similarity 0.88)
- Error Analysis Affected by Heavy-Tailed Gradients for Non-Convex Pairwise Stochastic Gradient Descent (AAAI 2025 · similarity 0.88)
- Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD (ICML 2024 · similarity 0.87)
- Investigating the Role of Weight Decay in Enhancing Nonconvex SGD (CVPR 2025 · similarity 0.87)
- Rapid Overfitting of Multi-Pass SGD in Stochastic Convex Optimization (ICML 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.