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Poster
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
Xinyi Hu · Jasper Lee · Jimmy Lee · Allen Z. Zhong

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #719

This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive algorithm satisfying simple conditions, we show how a corresponding learning algorithm can be constructed directly and methodically from the recursive algorithm. Our framework applies also to iterative algorithms by viewing them as a degenerate form of recursion. Extensive experimentation shows better performance for our proposal over classical and state of the art approaches.

Author Information

Xinyi Hu (The Chinese University of Hong Kong)
Jasper Lee (University of Wisconsin-Madison)
Jimmy Lee (The Chinese University of Hong Kong)
Allen Z. Zhong (The Chinese University of Hong Kong)

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