Browsing by Subject "Machine Learning"
Now showing items 1-20 of 63
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A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems
(2017)In this paper, we develop an algorithm that utilizes a Bayesian strategy to determine a sequence of questions to play the 20 Question game. The algorithm is motivated with an application to active recommender systems. We ... -
A Black-Scholes-integrated Gaussian Process Model for American Option Pricing
(2020-04-15)Acknowledging the lack of option pricing models that simultaneously have high prediction power, high computational efficiency, and interpretations that abide by financial principles, we suggest a Black-Scholes-integrated ... -
A Multi-Disciplinary Systems Approach for Modeling and Predicting Physiological Responses and Biomechanical Movement Patterns
(2017)It is currently an exciting time to be doing research at the intersection of sports and engineering. Advances in wearable sensor technology now enable large quantities of physiological and biomechanical data to be collected ... -
A Q-Learning Approach to Minefield Characterization from Unmanned Aerial Vehicles
(2012)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 ... -
A Search for Supersymmetry in Multi-b Jet Events with the ATLAS Detector
(2019)A search for supersymmetry in pair-produced gluinos decaying via top squarks to the lightest neutralino is presented. Events with multiple hadronic jets, of which at least three must be identified as originating from b-quarks, ... -
An Investigation into the Bias and Variance of Almost Matching Exactly Methods
(2021)The development of interpretable causal estimation methods is a fundamental problem for high-stakes decision settings in which results must be explainable. Matching methods are highly explainable, but often lack the accuracy ... -
Application of Stochastic Processes in Nonparametric Bayes
(2014)This thesis presents theoretical studies of some stochastic processes and their appli- cations in the Bayesian nonparametric methods. The stochastic processes discussed in the thesis are mainly the ones with independent ... -
Applying Machine Learning to Testing and Diagnosis of Integrated Systems
(2021)The growing complexity of integrated boards and systems makes manufacturing test and diagnosis increasingly expensive. There is a pressing need to reduce test cost and to pinpoint the root causes of integrated systems in ... -
Atlas Simulation: A Numerical Scheme for Approximating Multiscale Diffusions Embedded in High Dimensions
(2014)When simulating multiscale stochastic differential equations (SDEs) in high-dimensions, separation of timescales and high-dimensionality can make simulations expensive. The computational cost is dictated by microscale properties ... -
Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells.
(PloS one, 2016-01)Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection ... -
Automated Learning of Event Coding Dictionaries for Novel Domains with an Application to Cyberspace
(2016)Event data provide high-resolution and high-volume information about political events. From COPDAB to KEDS, GDELT, ICEWS, and PHOENIX, event datasets and the frameworks that produce them have supported a variety of research ... -
Bayesian and Information-Theoretic Learning of High Dimensional Data
(2012)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 ... -
Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas
(2016)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 ... -
CAUSAL INFERENCE FOR HIGH-STAKES DECISIONS
(2023)Causal inference methods are commonly used across domains to aid high-stakes decision-making. The validity of causal studies often relies on strong assumptions that might not be realistic in high-stakes scenarios. Inferences ... -
Characterizing and predicting the interaction of proteins with nanoparticles
(2023)Nanoparticles are being used or implemented in a wide array of applications including health care, cosmetics, automotive, food, beverage, coatings, consumer electronics, and coatings. Despite this widespread use, we are ... -
Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity.
(BMC medical informatics and decision making, 2022-03)<h4>Background</h4>Clinical decision support (CDS) tools built using adult data do not typically perform well for children. We explored how best to leverage adult data to improve the performance of such tools. This study ... -
Deep Automatic Threat Recognition: Considerations for Airport X-Ray Baggage Screening
(2020)Deep learning has made significant progress in recent years, contributing to major advancements in many fields. One such field is automatic threat recognition, where methods based on neural networks have surpassed ... -
Deep Generative Models for Vision, Languages and Graphs
(2019)Deep generative models have achieved remarkable success in modeling various types of data, ranging from vision, languages and graphs etc. They offer flexible and complementary representations for both labeled and unlabeled ... -
Deep Latent-Variable Models for Natural Language Understanding and Generation
(2020)Deep latent-variable models have been widely adopted to model various types of data, due to its ability to: 1) infer rich high-level information from the input data (especially in a low-resource setting); 2) result in a ... -
Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security
(2023)To judiciously use machine learning – particularly deep learning – requires identifying how to extract features from data and effectively leveraging those features to make predictions. This dissertation concerns deep learning ...
