CAREER: Self-adjusting Models as a New Direction in Machine Learning

机器学习算法现在通常用于从广泛应用的数据中构建预测模型。然而，当前的机器学习方法有一个重要的局限性：它们假设在训练数据集中观察到的类集是详尽的，并且新的数据样本来自训练数据集中表示的现有类之一。这个假设在许多现实世界的应用程序中是不现实的，在这些应用程序中会出现以前未观察到的感兴趣的类。本研究探索了一类新的机器学习算法，该算法产生自调整模型，可以适应离线和在线学习场景中数据中观察到的新类别。该项目旨在(i)使用非参数模型来动态地纳入不断变化的类别数量；（ii）开发新的在线和离线推理技术，以适应新出现的类；（iii）自动将新发现的类与更高级别的类组关联起来，试图识别潜在的有趣的类形成；（iv）开发部分观察到的树模型，包含观察到的和未观察到的节点。观察到的节点表示现有的类，未观察到的节点是在线引入的，以填补现有数据层次结构中的空白，这些空白只有在新数据到来时才会变得明显。这项工作的广泛影响可以扩展到几个现实世界的应用：生物安全和生物监测，信息检索和遥感等所有类别都不知道先验的设置。该教育计划包括扩展到K-12学生，并为计算机科学以及计算和生命科学交叉领域的本科生和研究生提供更多的研究机会。该项目产生的所有软件、出版物和数据集将免费分发给更大的研究和教育界。有关该项目的更多信息可通过该项目的网站http://www.cs.iupui.edu/~dundar/career.html获得

Machine learning algorithms are now routinely used to build predictive models from data in wide range of applications. However, current approaches to machine learning have an important limitation: They assume that the set of classes observed in a training data set is exhaustive and that new data samples originate from one of the existing classes represented in the training data set. This assumption is unrealistic in many real-world applications in which previously unobserved classes of interest emerge. This study explores a new class of machine learning algorithms that produce self-adjusting models that can accommodate new classes observed in data in offline as well as online learning scenarios. The project aims to (i) use non-parametric models to dynamically incorporate the changing number of classes; (ii) develop new online and offline inference techniques to accommodate new classes as they emerge (iii) automatically associate newly discovered classes with higher-level groups of classes in an attempt to identify potentially interesting class formations, and (iv) develop partially-observed tree models containing observed and unobserved nodes, where observed nodes represent existing classes and unobserved nodes are introduced online to fill the gaps in the existing data hierarchy that become evident only with the arrival of new data.The broader impacts of this work could extend to several real world applications: Bio-security and bio-surveillance, information retrieval, and remote sensing among others in settings where all of the classes are not known a priori. The educational plan includes outreach to K-12 students and enhanced research opportunities for undergraduate and graduate students in computer science as well as at the intersection of computational and life sciences. All the software, publications, and data sets resulting from the project will be freely disseminated to the larger research and educational community. Additional information about the project can be accessed through the project website at http://www.cs.iupui.edu/~dundar/career.html