III: Small: Collaborative Learning with Incomplete and Noisy Knowledge

大数据的加速增长为知识发现创造了宏观层面的海量信息。但在微观层面上，人们只能预期大多数个人用户会有少量的观察。这阻碍了在不同用户之间探索微妙的模式和异构性，以提高每个用户的大数据分析的效用。这个项目的目标是开发一套算法解决方案来以协作的方式执行在线学习，其中个性化的学习解决方案积极地与用户交互以获取反馈，并相互协作从不完整和有噪音的输入中学习。该项目扩大了统计学习在许多重要领域的效用，如医疗保健、商业智能、众包和网络物理系统，在这些领域，自动决策模型建立在多样化、嘈杂和异质监督的基础上。研究活动将纳入教材，供学生在信息检索、机器学习和数据挖掘领域进行培训和教育。该项目由三个协同研究推力组成。首先，它开发了一系列情境强盗算法，以在联网用户上执行协作式在线学习。估计用户之间的依赖关系，并利用该依赖关系协作更新个性化的盗贼参数。其次，开发了原则性解决方案来优化在线学习的特定任务和一般损失函数，使协作学习解决方案能够应用于更重要的现实应用，如信息检索和用户行为建模。第三，对反馈来源的可靠性进行建模和区分，以优化整体在线学习效果，这在健康信息学、众包和网络物理系统等应用中尤为重要。该项目的预期成果包括：1)开发在线学习解决方案的开放源码实施；2)评估语料库，使研究人员能够在相关领域开展后续研究。

The accelerated growth of Big Data has created enormous amount of information at the macro level for knowledge discovery. But at the micro level, one can only expect a handful of observations in most individual users. This hinders the exploration of subtle patterns and heterogeneities among distinct users for improving the utility of Big Data analytics at a per-user basis. The objective of this project is to develop a set of algorithmic solutions to perform online learning in a collaborative fashion, where personalized learning solutions actively interact with users for feedback acquisition and collaborate with each other to learn from incomplete and noisy input. This project amplifies the utility of statistical learning in many important fields, such as healthcare, business intelligence, crowdsourcing, and cyber physical systems, where automated decision models are built on diverse, noisy and heterogeneous supervision. The research activities will be incorporated into teaching materials for student training and education in the areas of information retrieval, machine learning and data mining. This project consists of three synergistic research thrusts. First, it develops a family of contextual bandit algorithms to perform collaborative online learning over networked users. Dependency among users is estimated and exploited to collaboratively update the individualized bandit parameters. Second, it develops principled solutions to optimize task-specific and general loss functions for online learning, which enables the collaborative learning solutions reach more important real-world applications, such as information retrieval and user behavior modeling. Third, it models and differentiates the reliability of the sources of feedback to optimize the overall online learning effectiveness, which is especially important in the applications such as health informatics, crowdsourcing and cyber physical systems. Expected outcomes of the project include: 1) open source implementations for the developed online learning solutions; and 2) evaluation corpora that will enable researchers to conduct follow-up research in related domains.