Poster
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
Rasmus Kyng · Anup Rao · Sushant Sachdeva
210 C #86
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Abstract
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Abstract:
Given a directed acyclic graph and a set of values on the vertices, the Isotonic Regression of is a vector that respects the partial order described by and minimizes for a specified norm. This paper gives improved algorithms for computing the Isotonic Regression for all weighted -norms with rigorous performance guarantees. Our algorithms are quite practical, and their variants can be implemented to run fast in practice.
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