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Poster
Computational and Statistical Tradeoffs in Learning to Rank
Ashish Khetan · Sewoong Oh

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #10
in Tuesday Posters »

For massive and heterogeneous modern data sets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of rank-breaking mechanisms ordered by the complexity in thus generated sketch of the data. This allows the number of data points collected to be gracefully traded off against computational resources available, while guaranteeing the desired level of accuracy. Theoretical guarantees on the proposed generalized rank-breaking implicitly provide such trade-offs, which can be explicitly characterized under certain canonical scenarios on the structure of the data.

Keywords: Convex Optimization · Ranking and Preference Learning

Author Information

Ashish Khetan (University of Illinois Urbana-)
Sewoong Oh (University of Washington)

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