AF: Small: Learning and Optimization with Strategic Data Sources

本研究项目的目标是当机器学习的训练数据或关于优化问题的信息从战略来源获得时，在机器学习和优化方面开发新的结果。我们拥有前所未有的能力与世界各地的人们联系：购买和销售产品，分享信息和经验，提问和回答问题，在项目上合作，借入和借出资金，以及交换多余的资源。这些活动产生了丰富的数据，科学家可以利用这些数据来理解人类的社会行为，产生准确的预测，找到疾病的治疗方法，并提出政策建议。机器学习和优化传统上将这些数据视为给定的，例如将它们视为从某个未知概率分布中提取的独立样本。然而，这些数据是由人们在特定的交互规则的背景下拥有或生成的。因此，哪些数据可用以及可用数据的质量是战略决策的结果。例如，患有敏感疾病的人可能不太愿意在调查中透露他们的医疗数据，自由职业者可能不会真诚地努力完成一项任务。数据的这一战略方面挑战了机器学习和优化中的基本假设。该研究项目以整体的视角，将数据获取与学习和优化结合起来考虑。它将在广泛应用机器学习和优化的商业、政府和社会决策过程中带来更好的好处。该研究项目还包括对博士生的指导，研究生教学的创新，以及未被充分代表的群体成员参与研究。PI将追求广泛的研究议程，发展对从战略来源获取数据如何影响机器学习和优化目标的基本理解。第一组目标旨在开发一种机器学习理论，当学习算法需要从数据持有者那里购买数据时，这些数据持有者无法伪造数据，但每个人都有与披露数据相关的私人成本。将建立机器学习的经济效率的概念。第二组目标将通过设计联合启发和学习机制来进一步推进机器学习的前沿，当数据从战略代理获得时，但贡献的数据的质量无法直接验证。第三组目标是当优化问题的参数最初未知时，开发具有良好理论保证的优化算法，但算法设计者可以从策略代理收集有关参数的信息。

The goal of this research project is to develop new results in machine learning and optimization when training data for machine learning or information about optimization problems is acquired from strategic sources. We are blessed with unprecedented abilities to connect with people all over the world: buying and selling products, sharing information and experiences, asking and answering questions, collaborating on projects, borrowing and lending money, and exchanging excess resources. These activities result in rich data that scientists can use to understand human social behavior, generate accurate predictions, find cures for diseases, and make policy recommendations. Machine learning and optimization traditionally take such data as given, for example treating them as independent samples drawn from some unknown probability distribution. However, such data are possessed or generated by people in the context of specific rules of interaction. Hence, what data become available and the quality of available data are results of strategic decisions. For example, people with sensitive medical conditions may be less willing to reveal their medical data in a survey and freelance workers may not put in a good-faith effort in completing a task. This strategic aspect of data challenges fundamental assumptions in machine learning and optimization. The research project takes a holistic view that jointly considers data acquisition with learning and optimization. It will bring improved benefits in business, government, and societal decision-making processes where machine learning and optimization are widely applicable. The research project also involves the mentoring of PhD students, innovation in graduate teaching, and engagement of members of underrepresented groups in research.The PI will pursue a broad research agenda developing a fundamental understanding of how acquiring data from strategic sources affects the objectives of machine learning and optimization. The first set of goals aims to develop a theory for machine learning when a learning algorithm needs to purchase data from data holders who cannot fabricate their data but each have a private cost associated with revealing their data. A notion of economic efficiency for machine learning will be established. The second set of goals will further advance the frontier of machine learning by designing joint elicitation and learning mechanisms when data are acquired from strategic agents but the quality of the contributed data cannot be directly verified. The third set of goals will develop optimization algorithms with good theoretical guarantees when parameters of an optimization problem may be unknown initially but the algorithm designer can gather information about the parameters from strategic agents.