Effective learning of user preferences is critical to easing user burden in various types of matching problems. Equally important is active query selection to further reduce the amount of preference information users must provide. We address the problem of active learning of user preferences for matching problems, introducing a novel method for determining probabilistic matchings, and developing several new active learning strategies that are sensitive to the specific matching objective. Experiments with real-world data sets spanning diverse domains demonstrate that matching-sensitive active learning
An information-matching approach to optimal experimental design and active learning
Brigham Young University；Achilles Heel Technologies；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；University of Electronic Science and Technology of China（电子科学与技术大学）；University of Minnesota（明尼苏达大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
Inverse design of bespoke interatomic potentials via active learning by information-matching
Department of Physics and Astronomy, Brigham Young University（物理学与天文学系， Brigham Young 大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Department of Aerospace Engineering and Mechanics, University of Minnesota（航空航天工程与力学系，明尼苏达大学）；Department of Materials Science and Engineering, University of California（材料科学与工程系，加州大学）；Cross Stream Consulting（Cross Stream 咨询）
Interactive Ontology Matching with Cost-Efficient Learning
NEC Laboratories Europe（欧洲 NEC 实验室）；Springer Nature（施普林格·自然）；Zurich University of Applied Sciences（苏黎世应用科技大学）；Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）
