Yuehua Xu

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Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted action-selection rules for guiding greedy search with application to automated planning. We make two primary contributions over prior work on learning for greedy search. First, we(More)
Beam search is commonly used to help maintain tractability in large search spaces at the expense of completeness and optimality. Here we study supervised learning of linear ranking functions for controlling beam search. The goal is to learn ranking functions that allow for beam search to perform nearly as well as unconstrained search, and hence gain(More)
Constraint-based problems are hard combinatorial problems and are usually solved by heuristic search methods. In this paper, we consider applying a machine learning approach to improve the performance of these search-based solvers. We apply reinforcement learning in the context of Constraint Satisfaction Problems (CSP) to learn a value function, which(More)
Observation of tongue coating, a foundation for clinical diagnosis and treatment in traditional Chinese medicine (TCM), is a major indicator of the occurrence, development, and prognosis of disease. The biological basis of tongue diagnosis and relationship between the types and microorganisms of tongue coating remain elusive. Thirteen chronic erosive(More)
UNLABELLED : Identifying chemical probes or seeking scaffolds for a specific biological target is important for protein function studies. Therefore, we create the Annotated Scaffold Database (ASDB), a computer-readable and systematic target-annotated scaffold database, to serve such needs. The scaffolds in ASDB were derived from public databases including(More)
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