New Theoretical Frameworks for Machine Learning
CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
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This thesis has two primary thrusts. The first is developing new models and algorithms for important modern and classic learning problems. The second is establishing new connections between Machine Learning and Algorithmic Game Theory. The formulation of the PAC learning model by Valiant 201 and the Statistical Learning Theory framework by Vapnik 203 have been instrumental in the development of machine learning and the design and analysis of algorithms for supervised learning. However, while extremely influential, these models do not capture or explain other important classic learning paradigms such as Clustering, nor do they capture important emerging learning paradigms such as Semi-Supervised Learning and other ways of incorporating unlabeled data in the learning process. In this thesis, we develop the first analog of these general discriminative models to the problems of Semi-Supervised Learning and Clustering, and we analyze both their algorithmic and sample complexity implications. We also provide the first generalization of the well established theory of learning with kernel functions to case of general pair wise similarity functions and in addition provide new positive theoretical results for Active Learning. Finally, this dissertation presents new applications of techniques from Machine Learning to Algorithmic Game Theory, which has been a major area of research at the intersection of Computer Science and Economics. In machine learning, there has been growing interest in using unlabeled data together with labeled data due to the availability of large amounts of unlabeled data in many contemporary applications. As a result, a number of different semi-supervised learning methods such as Co-training, transductive SVM, or graph based methods have been developed. However, the underlying assumptions of these methods are often quite distinct and not captured by standard theoretical models.