Information Procuration via Adaptive Algorithms

主要研究者（PI）制定的信息采购问题，将非结构化数据转换为结构化信息，作为一个使用有限的资源（如处理时间和收集成本）之间的几个可用的策略，信息采集，提取，整理和聚合在一个顺序和自适应的方式。该建议旨在建立一个马尔可夫决策过程（MDP）的状态和奖励将学习，并从中提取有效的信息procuration.Recent方法设计的自适应策略的多臂强盗问题和预算学习方法的PI预算将为此目的，以及从逆强化学习技术的最佳自适应策略。此外，考虑到应用程序数据集的预期大小，重点将放在这些问题的可扩展算法上。由于问题的中心性，更好地理解非结构化数据的新方法将在开发新方法和实践方面产生很大影响。在这种情况下，从运筹学，近似算法和机器学习的方法的综合建议是新颖的。该提案将通过在这个交叉点形成的协作团队，增加运筹学和机器学习之间的思想交流。

The Principal Investigator (PI) formulates the information procuration problem of converting unstructured data into structured information as one of using limited resources (such as processing time and collection costs) among several available strategies for information acquisition, extraction, collation and aggregation in a sequential and adaptive manner. The proposal aims to build a Markov decision process (MDP) for which both the states and the rewards will be learned, and from which an optimal adaptive strategy for effective information procuration will be extracted.Recent methods for designing adaptive strategies for multi-armed bandit problems and budgeted learning approaches by the PI will be extended for this purpose, as well as techniques from inverse reinforcement learning. Moreover, given the intended size of the application data sets, the focus will be on on scalable algorithms for these problems. Due to the centrality of the problem, new approaches to making better sense of unstructured data will have much impact both in terms of developing new methods and in practice. The proposed synthesis of methods from Operations Research, Approximation Algorithms and Machine Learning is novel in this context. This proposal will increase the cross-fertilization of ideas between Operations Research and Machine Learning, via a collaboration team formed at this intersection.