Research map / Learning theory and algorithms / Prediction and decision losses
The Complete Lasso Tradeoff Diagram
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 |
|---|---|---|---|
| Increase the noise level from 0 toward sufficiently large values | Lasso · Simulated Lasso tradeoff diagrams | FDP | Hurt |
| Use an i.i.d. Cauchy design instead of the Gaussian design assumptions | Lasso · Simulated tradeoff diagram, k/p=0.3, n/p=0.7 | FDP lower boundary | Hurt |
| Use a Gaussian design with AR(0.05) covariance or an i.i.d. Bernoulli(0.5) design | Lasso · Simulated tradeoff diagram, k/p=0.3, n/p=0.7 | Tradeoff diagram agreement | No clear effect |
Closest papers by meaning
- Statistical-Computational Tradeoff in Single Index Models (NeurIPS 2019 · similarity 0.88)
- A Comparison of Hamming Errors of Representative Variable Selection Methods (ICLR 2022 · similarity 0.86)
- Strong and Weak Identifiability of Optimization-based Causal Discovery in Non-linear Additive Noise Models (ICML 2025 · similarity 0.85)
- Robust Sparse Regression with Non-Isotropic Designs (NeurIPS 2024 · similarity 0.85)
- Regression Learning with Limited Observations of Multivariate Outcomes and Features (ICML 2024 · similarity 0.85)
- CoLiDE: Concomitant Linear DAG Estimation (ICLR 2024 · similarity 0.84)
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.
