AI Paper Atlas

Research map / Optimization / Nonconvex and smooth optimization

Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods

NeurIPS 2016 · Optimization › Nonconvex and smooth optimization · approach: Coordinate and Adam convergence analysis · 38 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
GFN instead of untuned Supervised PageRankSupervised PageRank · Q1_2loss: .00357 -> .00274Helped
GBN instead of GBP 50 stepsGBP · Q1_2loss: .00282 -> .00279Helped
GFN instead of GBP 50 stepsGBP · Q2_2loss: .00307 -> .00297Helped
GBN instead of GBP 50 stepsGBP · Q3_2loss: .00295 -> .00295No clear effect
GBN instead of GBP step size 50GBP · Q2_2upper-level steps: 31 -> 12Helped
GFN instead of GBNGBN · Q3_2upper-level steps: 12 -> 106Hurt
increase GBP step size from 50 to 500GBP · Q1_2loss: .00282 -> .00283No clear effect
increase GBP step size from 50 to 500GBP · Q1_2upper-level steps: 16 -> 2Helped
vary GBN initial Lipschitz constant L0 from 1e-4 to 1 (ablation)GBN · all test setsloss: differences are less than 10−7No clear effect
tighten accuracy epsilon below 1e-6 (ablation)GBN and GFN · Q1_2-Q3_2lossNo 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