atlas

Research map / Learning theory and algorithms / Prediction and decision losses

Data-driven Rank Breaking for Efficient Rank Aggregation

ICML 2016 · Learning theory and algorithms › Prediction and decision losses · approach: Ranking models from comparisons · 21 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
Use data-driven weights instead of uniform weights for consistent rank-breaking (ablation)Rank-breaking on top-ℓ separators · Top-ℓ separators, simulated PL datamean squared errorHelped
Use data-driven rank-breaking instead of GMMPL rank aggregation · Sushi preferencesmean squared errorHelped
Use data-driven rank-breaking instead of GMMPL rank aggregation · Jestermean squared errorHelped
Use data-driven rank-breaking instead of GMMPL rank aggregation · APA election preferencesmean squared errorHelped
Use data-driven rank-breaking instead of GMMPL rank aggregation · Sushi preferencesKendall rank correlationHelped
Use data-driven rank-breaking instead of other aggregation methodsSushi preferences · Sushi, n = 5000, ℓ = 5Kendall rank correlation: 0.217 vs 0.200Mixed
Choose separator positions uniformly across the offered items instead of restricting them to the top (ablation)Data-driven rank-breaking · Simulated PL rankingsmean squared errorHelped
Place separators at the bottom rather than across the ranking (ablation)Data-driven rank-breaking · Bottom-ℓ separatorsmean squared errorHurt
Use a comparison graph with a larger spectral gap (ablation)Data-driven rank-breaking · Simulated comparison graph topologiesmean squared errorHelped
Use random pairwise comparisons with uniform weights instead of consistent data-driven rank-breaking (ablation)Rank-breaking · Random separator positionsmean squared errorMixed
Use optimal weights instead of uniform weights when offering-set sizes are heterogeneous (ablation)Consistent rank-breaking · Mixed top-ℓ and top-1 comparisonsmean squared error: roughly 10 times worseHelped
Use optimal weights instead of uniform weights with fixed offering-set size (ablation)Consistent rank-breaking · Separators at the top and bottommean squared errorHelped

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