Research map / Learning theory and algorithms / Sample complexity
Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
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 Multivariate Robust Mean Estimation Under Mean-Shift Contamination (ICML 2025 · similarity 0.93)
- Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination (ICML 2024 · similarity 0.87)
- The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination (NeurIPS 2025 · similarity 0.84)
- Robust Estimation Under Heterogeneous Corruption Rates (NeurIPS 2025 · similarity 0.84)
- Mean Testing under Truncation beyond Gaussian (NeurIPS 2026 · similarity 0.84)
- A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers (NeurIPS 2024 · similarity 0.83)
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.
