2022/05/01 by Xinyi Hu, Jasper C. H. Lee, Hu, Xinyi +5
Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2205.01672
openalex publication_date 2022/05/01 · openalex created_date 2022/05/08 · openalex updated_date 2026/07/28
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.