Research map / Learning theory and algorithms / Sample complexity
SQ Lower Bounds for Learning Mixtures of Linear Classifiers
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
- Sample-Efficient Private Learning of Mixtures of Gaussians (NeurIPS 2024 · similarity 0.86)
- Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification (NeurIPS 2026 · similarity 0.86)
- The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures (ICLR 2025 · similarity 0.85)
- Is $\sqrt{d}$ Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions? (NeurIPS 2026 · similarity 0.85)
- Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-Index Models (NeurIPS 2025 · similarity 0.85)
- Learning multivariate Gaussians with imperfect advice (ICML 2025 · similarity 0.85)
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.