UC San Diego

In interactive machine learning, the learning machine is engaged in some fashion with an information source (e.g. a human or another machine). In this thesis, we study frameworks for interactive machine learning.

In the first part, we consider interaction in supervised learning. The typical model of interaction in supervised learning has been restricted to labels alone. We study a framework in which the learning machine can receive feedback that goes beyond labels of data points, to features that may be indicative of a particular label. We call this framework learning with feature feedback and study it formally in several settings.

In the second part, we study interaction in unsupervised learning, in particular, topic modeling. Topic models are popular tools for analyzing large text corpora. However, the topics discovered by a topic model are often not meaningful to practitioners. We study two different interactive protocols for topic modeling that allow users to address deficiencies and build models that yield meaningful topics.

In the third part, we study interactive machine teaching. Different from traditional machine teaching, in which teachers do not interact with the learners, we study a framework in which interactive teachers can efficiently teach any concept to any learner.