Indraneel Mukherjee will present his preFPO on Tuesday November 16 at 2PM in Room 402. The members of his committee are: Rob Schapire, advisor; Yoav Freund (UCSD) and David Blei, readers; Moses Charikar and Philippe Rigollet (ORFE), nonreaders. Everyone is invited to attend his talk. His abstract and reading list follow below. Title: Results on Boosting: Abstract: Boosting is a highly popular off-the-shelf machine learning algorithm. Yet its understanding is incomplete. In this thesis, we study two theoretical questions related to boosting. In the first project, we study boosting algorithms for multiclass classification. Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain game-theoretic sense, boosting algorithms that assume such requirements. Secondly, we consider the problem of learning to predict as well as the best in a group of experts making continuous predictions, which is related to finding the optimal boosting algorithm with confidence rated weak-learners. We propose a new master strategy that achieves the best possible performance when the number of experts is sufficiently high. Our ideas are based on drifting games, a generalization of boosting and on-line learning algorithms. A surprising consequence of our work is that continuous experts are only as powerful as experts making binary or no prediction in each round.