AI Paper Atlas

Research map / Optimization / Optimizers

Fast OSCAR and OWL Regression via Safe Screening Rules

ICML 2020 · Optimization › Optimizers · approach: Surrogate losses and robust solvers · 8 citations (OpenAlex)

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 changedWhereMeasuredEffect
Add safe screening to APGDAPGD for OSCAR/OWL regression · When n ≪ dcomputational gain: 4x to 8xHelped
Add safe screening to SPGDSPGD for OSCAR/OWL regression · When n ≪ dcomputational gain: 5x to 22xHelped
Add safe screening to SPGDSPGD for OSCAR/OWL regression · Large-scale learning where n ≈ d or n ≫ dcomputational gain: 4x to 40xHelped
Add safe screening to APGD or SPGDOWL regression · Duke Breast Cancer, Colon Cancer, Cardiac Left, Cardiac Right, IndoorLoc Longitude, and Slice LocalizationRMSE: 0.6523 -> 0.6523; 0.9453 -> 0.9453; 0.4756 -> 0.4756; 0.5276 -> 0.5276; 0.5531 -> 0.5531; 0.6162 -> 0.6162No clear effect

Closest papers by meaning

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.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy