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Poster
Accuracy at the Top
Stephen Boyd · Corinna Cortes · Mehryar Mohri · Ana Radovanovic

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None
in Poster Session »
We introduce a new notion of classification accuracy based on the top $\tau$-quantile values of a scoring function, a relevant criterion in a number of problems arising for search engines. We define an algorithm optimizing a convex surrogate of the corresponding loss, and show how its solution can be obtained by solving several convex optimization problems. We also present margin-based guarantees for this algorithm based on the $\tau$-quantile of the functions in the hypothesis set. Finally, we report the results of several experiments evaluating the performance of our algorithm. In a comparison in a bipartite setting with several algorithms seeking high precision at the top, our algorithm achieves a better performance in precision at the top.
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Author Information

Stephen Boyd (Stanford University)
Corinna Cortes (Google Research)
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)
Ana Radovanovic (Google Research)

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