Prediction in financial domains is notoriously difficult for a number ofreasons. First, theories tend to be weak or non-existent, which makesproblem formulation open ended by forcing us to consider a large numberof independent variables and thereby increasing the dimensionality ofthe search...

In many classification tasks training data have missing feature valuesthat can be acquired at a cost. For building accurate predictive models,acquiring all missing values is often prohibitively expensive orunnecessary, while acquiring a random subset of feature values may notbe most effective....

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings...

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of...

This dissertation discusses three problems from different areas of medical research and their machine learning solutions. Each solution is a distinct type of decision support system. They show three common properties: personalized healthcare decision support, reduction of the use of medical...

The vast majority of the literature related to the empirical estimation of retention models includes a discussion of the theoretical retention framework established by Bean, Braxton, Tinto, Pascarella, Terenzini and others (see Bean, 1980; Bean, 2000; Braxton, 2000; Braxton et al, 2004; Chapman and Pascarella,...

The problem: Much of finance theory is based on the efficient market hypothesis. According to this hypothesis, the prices of financial assets, such as stocks, incorporate all information that may affect their future performance. However, the translation of publicly available information into...

Bayesian nonparametric methods are useful for modeling data without having to define the complexity of the entire model a priori, but rather allowing for this complexity to be determined by the data. Two problems considered in this dissertation are the number of components in a mixture model,...

We present systems approaches for engineering medical and the healthcare delivery processes. Part 1 of the talk focuses on medical care processes. Specifically, we discuss our innovative technological advances in health and (bio)medical informatics that assist in health risk prediction, early...

Learning algorithms based on the Fourier transform attempt to learn functions by approximating the largest coefficients of their Fourier representations. Nearly all previous work in Fourier-based learning has been in the theoretical realm, where properties of the transform have made it possible...