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Computational psychiatry: machine learning for clinical decision support in the treatment of major depression
DownloadFall 2019
The goal of this thesis is to contribute to the fields of data-driven medicine and computational psychiatry by attempting to demonstrate the viability of machine learning for use in psychiatry, specifically in predicting treatment outcomes for major depression. This is attempted in four ways: ...
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Fall 2022
In recent years, conversational agents have been widely used in education to support student learning. Conversational agents have the capability to enhance learning by improving interaction, motivation, feedback, and personalization. To date, researchers have designed and used different types of...
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Spring 2020
Several Artificial Intelligence (AI) techniques such as machine learning, evolutionary computing, and Artificial Life (A-life) have been increasingly used to generate emergence of novel behaviours in multi-agent simulations (e.g., commercial games). However, automatically detecting emergent...
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Spring 2021
Temporal difference (TD) methods provide a powerful means of learning to make predictions in an online, model-free, and highly scalable manner. In the reinforcement learning (RL) framework, we formalize these prediction targets in terms of a (possibly discounted) sum of rewards, called the...
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Fall 2018
The objective of this work is to design a scheme to control the power flow of a hybrid renewable energy system with multiple renewable energy sources with the focus on solar energy and wind energy and multiple energy storage systems. The use of energy storage is necessary due to the intermittency...
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Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
DownloadFall 2020
Policy gradient methods typically estimate both explicit policy and value functions. The long-extant view of policy gradient methods as approximate policy iteration---alternating between policy evaluation and policy improvement by greedification---is a helpful framework to elucidate algorithmic...
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Spring 2023
Knowledge graphs are data storage structures that rely on principles from graph theory to represent information. Specifically, facts are stored as triples which bring together two entities via a predicate. In a graphical context, these entities are analogous to nodes, and the relations between...
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Learning Individual Readmission-Free Survival Distributions using Longitudinal Medical Events
DownloadFall 2023
The rate of 30-day hospital readmission is a common measurement of hospital quality, which can affect the funding a hospital receives. Over a quarter of readmissions are estimated to be preventable with adequate interventions, but these interventions are themselves costly. For this reason, many...
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Predicting Uterine Deformation Due to Applicator Insertion in Pre-Brachytherapy MRI Using Deep Learning
DownloadSpring 2023
In locally advanced cervical cancer (LACC), brachytherapy (BT) remains the gold standard for boosting to curative doses in radiotherapy. Progress towards balancing target and routine tissue dosimetry for better clinical outcomes has been made possible by magnetic resonance imaging (MRI)-guided...
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Fall 2023
Curiosity appears to motivate and guide effective learning in humans, which has led to high hopes in the machine learning community for machine analogues of curiosity. While a variety of machine curiosity algorithms have been introduced, they are rarely compared with other existing curiosity...