Research map / Learning theory and algorithms / Kernels and regression theory
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
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 Generalization Error of Dictionary Learning with Moreau Envelopes (ICML 2018 · similarity 0.87)
- Minimum Norm Interpolation Meets The Local Theory of Banach Spaces (ICML 2024 · similarity 0.87)
- Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting (NeurIPS 2021 · similarity 0.86)
- Multiclass Loss Geometry Matters for Generalization of Gradient Descent in Separable Classification (NeurIPS 2025 · similarity 0.85)
- Beyond Benign Overfitting in Nadaraya-Watson Interpolators (NeurIPS 2025 · similarity 0.84)
- Scalable First-order Method for Certifying Optimal k-Sparse GLMs (ICML 2025 · similarity 0.84)
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.