Research map / Learning theory and algorithms / Sample complexity
SOL: Sampling-based Optimal Linear bounding of arbitrary scalar functions
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
- Efficient Neural Network Robustness Certification with General Activation Functions (NeurIPS 2018 · similarity 0.91)
- On the Complexity of Learning Neural Networks (NeurIPS 2017 · similarity 0.88)
- Understanding Certified Training with Interval Bound Propagation (ICLR 2024 · similarity 0.86)
- Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations (ICLR 2024 · similarity 0.86)
- Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods (ICLR 2025 · similarity 0.86)
- Generalization Bounds for Rank-sparse Neural Networks (NeurIPS 2025 · 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.
