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Spring 2017
Optimizing an objective function over convex sets is a key problem in many different machine learning models. One of the various kinds of well studied objective functions is the convex function, where any local minimum must be the global mini- mum over the domain. To find the optimal point that...
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Fall 2012
Functional Magnetic Resonance Imaging (fMRI) measures the dynamic activity of each voxel of a brain. This dissertation addresses the challenge of learning a diagnostic classifier that uses a subject’s fMRI data to distinguish subjects with neuropsychiatric disorders from healthy controls. fMRI...
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Fall 2017
On the one hand, theoretical analyses of machine learning algorithms are typically performed based on various probabilistic assumptions about the data. While these probabilistic assumptions are important in the analyses, it is debatable whether such assumptions actually hold in practice. Another...
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Fall 2023
Many real-world tasks in fields such as robotics and control can be formulated as constrained Markov decision processes (CMDPs). In CMDPs, the objective is usually to optimize the return while ensuring some constraints being satisfied at the same time. The primal-dual approach is a common...
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Fall 2023
The recognition performance of Optical Character Recognition (OCR) models can be sub-optimal when document images suffer from various degradations. Supervised learning-based methods for image enhancement can generate high-quality enhanced images. However, these methods require the availability of...