{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ml4co-kida-knowledge-inheritance-in-data","title":"ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation","arxiv_id":"2201.10328","date":"2022-01-25","proceeding":null,"authors":["Zixuan Cao","Yang Xu","Zhewei Huang","Shuchang Zhou"],"abstract":"The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models. On the dual task, we design models to make branching decisions to promote the dual bound increase faster. We propose a knowledge inheritance method to generalize knowledge of different models from the dataset aggregation process, named KIDA. Our improvement overcomes some defects of the baseline graph-neural-networks-based methods. Further, we won the $1$\\textsuperscript{st} Place on the dual task. We hope this report can provide useful experience for developers and researchers. 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