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Nonparametric Bayesian Models for Joint Analysis of Imagery and Text
It has been increasingly important to develop statistical models to manage large-scale high-dimensional image data. This thesis presents novel hierarchical nonparametric Bayesian models for joint analysis of imagery and ...
Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas
Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden ...
Bayesian and Information-Theoretic Learning of High Dimensional Data
The concept of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated ...
Learning from Geometry
Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple ...
A Q-Learning Approach to Minefield Characterization from Unmanned Aerial Vehicles
The treasure hunt problem to determine how a computational agent can maximize its ability to detect and/or classify multiple targets located in a region of interest (ROI) populated with multiple obstacles. One particular ...