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

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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.