Models and algorithms for interactive machine learning applied to formal languages and geometric concepts

应用机器学习中的一个中心问题是，通常需要比可获得或负担得起的数据量更多的数据，例如，当必须进行昂贵的实验室实验来生成数据时，就像生物医学研究中经常出现的情况一样，或者当必须学习涉及基于计算机的系统的单个用户的模式(而不是涉及大量用户的模式)时。我提出的计算学习理论领域的研究是通过交互式机器学习理论来解决这个问题的。这里的交互是指学习算法或环境主动控制交换关于要学习的目标对象的哪些信息。交互式机器学习对于各种应用具有很高的相关性，例如，其中人与学习系统交互并被学习系统观察的应用。我的目标是设计和分析交互学习的正式模型，并开发算法技术，以比目前可能的数据更少的情况下有效地解决复杂的学习问题。

A central problem in applied machine learning is that often data is required in larger quantities than are available or affordable, for instance, when costly lab experiments have to be conducted to generate data, as is often the case in biomedical research, or when patterns concerning a single user of a computer-based system (rather than patterns concerning a large pool of users) have to be learned. My proposed research in the field of computational learning theory addresses this problem by means of the theory of interactive machine learning. Interaction here means that the learning algorithm or the environment actively controls which information is exchanged about the target object to be learned. Interactive machine learning is of high relevance for a variety of applications, e.g., those in which a human interacts with and is observed by a learning system. My objective is to design and analyze formal models of interactive learning and to develop algorithmic techniques that can efficiently solve complex learning problems with less data than is currently possible.