Research map / Learning theory and algorithms / Sample complexity
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions
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 |
|---|---|---|---|
| Drop the finite chi-squared-distance assumption and retain moment matching | NGCA SQ-hardness analysis · NGCA | SQ lower-bound strength | Helped |
| Use the moment-matching-only NGCA lower bound | List-decodable Gaussian mean estimation · List-decodable Gaussian mean estimation | SQ query tolerance lower bound | Helped |
| Use the moment-matching-only NGCA lower bound | Anti-concentration detection · Anti-concentration detection | SQ query tolerance lower bound | Helped |
| Use the moment-matching-only NGCA lower bound | Periodic-function learning · Learning periodic functions | SQ query tolerance lower bound | Helped |
Closest papers by meaning
- An Optimized Franz-Parisi Criterion and its Equivalence with SQ Lower Bounds (NeurIPS 2025 · similarity 0.86)
- Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals (NeurIPS 2020 · similarity 0.84)
- Statistical-Computational Trade-offs for Density Estimation (NeurIPS 2024 · similarity 0.82)
- Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-Index Models (NeurIPS 2025 · similarity 0.82)
- How Accurately Can a Gaussian Approximate Stochastic Approximation Iterates? (NeurIPS 2026 · similarity 0.82)
- Sub-Gaussian Confidence Intervals for Heavy-Tailed Data: Characterizing the Limits of Inference (NeurIPS 2026 · similarity 0.81)
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.