Active Value Querying to Minimize Additive Error in Subadditive Set Function Learning

ArXi:2602.23529v2 Announce Type: replace Subadditive set functions play a pivotal role in computational economics (especially in combinatorial auctions), combinatorial optimization or artificial intelligence applications such as interpretable machine learning. However, specifying a set function requires assigning values to an exponentially large number of subsets in general, a task that is often resource-intensive in practice, particularly when the values derive from external sources such as re