English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper studies the problem of finding the exact ranking from noisycomparisons. A comparison over a set of $m$ items produces a noisy outcomeabout the most preferred item, and reveals some information about the ranking.By repeatedly and adaptively choosing items to compare, we want to fully rankthe items with a certain confidence, and use as few comparisons as possible.Different from most previous works, in this paper, we have three mainnovelties: (i) compared to prior works, our upper bounds (algorithms) and lowerbounds on the sample complexity (aka number of comparisons) require the minimalassumptions on the instances, and are not restricted to specific models; (ii)we give lower bounds and upper bounds on instances with unequal noise levels;and (iii) this paper aims at the exact ranking without knowledge on theinstances, while most of the previous works either focus on approximaterankings or study exact ranking but require prior knowledge. We first derivelower bounds for pairwise ranking (i.e., compare two items each time), and thenpropose (nearly) optimal pairwise ranking algorithms. We further makeextensions to listwise ranking (i.e., comparing multiple items each time).Numerical results also show our improvements against the state of the art.

On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons