Research map / Learning theory and algorithms / Sample complexity
Tester-Learners for Halfspaces: Universal Algorithms
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Test hypercontractivity with an SOS program rather than checking moments against one fixed target distribution | Universal tester-learner for halfspaces · Nice, Poincaré marginals | classification error guarantee: O(\mathsf{opt}) + \epsilon | Helped |
| Use the Massart-specific analysis and guarantee | Universal tester-learner for Massart halfspaces · Nice, Poincaré marginals under Massart noise | classification error guarantee: \mathsf{opt} + \epsilon | Helped |
Closest papers by meaning
- An Efficient Tester-Learner for Halfspaces (ICLR 2024 · similarity 0.91)
- Testably Learning Polynomial Threshold Functions (NeurIPS 2024 · similarity 0.88)
- Reliable Learning of Halfspaces under Gaussian Marginals (NeurIPS 2024 · similarity 0.88)
- Smoothed Agnostic Learning of Halfspaces over the Hypercube (NeurIPS 2025 · similarity 0.86)
- A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise (NeurIPS 2024 · similarity 0.86)
- Replicable Learning of Large-Margin Halfspaces (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.