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

The classical analysis of online algorithms, due to its worst-case nature,can be quite pessimistic when the input instance at hand is far fromworst-case. Often this is not an issue with machine learning approaches, whichshine in exploiting patterns in past inputs in order to predict the future.However, such predictions, although usually accurate, can be arbitrarily poor.Inspired by a recent line of work, we augment three well-known online settingswith machine learned predictions about the future, and develop algorithms thattake them into account. In particular, we study the following online selectionproblems: (i) the classical secretary problem, (ii) online bipartite matchingand (iii) the graphic matroid secretary problem. Our algorithms still come witha worst-case performance guarantee in the case that predictions are subparwhile obtaining an improved competitive ratio (over the best-known classicalonline algorithm for each problem) when the predictions are sufficientlyaccurate. For each algorithm, we establish a trade-off between the competitiveratios obtained in the two respective cases.

Secretary and Online Matching Problems with Machine Learned Advice