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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 83Machine Learning
- 76Reinforcement Learning
- 42Artificial Intelligence
- 37Machine learning
- 24Natural Language Processing
- 23reinforcement learning
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Spring 2024
In model-based reinforcement learning, an agent can improve its policy by planning: learning from experience generated by a model. Search control is the problem of determining which starting state should be used to generate this experience. Given a limited planning budget, an agent should be...
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Spring 2011
In this thesis, we study theoretically and empirically the additive abstraction-based heuristics. First we present formal general definitions for abstractions that extend to general additive abstractions. We show that the general definition makes proofs of admissibility, consistency, and...
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Spring 2016
In this work we address the challenges arising when developing, testing and deploying software for Wireless Sensor Networks. We investigate both pre-deployment software design, as well as efficient post-deployment updates. We present a combined pre-deployment framework that simulates the network...
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Addressing the Challenges of Applying Machine Learning for Predicting Mental Disorders and Their Prognosis Using Two Case Studies
DownloadSpring 2019
Ghoreishiamiri, Seyedehreyhaneh
One of the principal applications of machine learning in psychiatry is to build automated tools that can help clinicians predict the diagnosis and prognosis of mental disorders using available data from patients’ profiles. Here, in two different studies, we investigate ways to use machine learn-...
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Advanced Machine Learning Techniques for Analysis of 4D Brain Images in Ischemic Stroke Diagnosis and Assessment
DownloadFall 2023
Acute Ischemic Stroke (AIS), a devastating cerebrovascular disorder, is one of the leading causes of disability and mortality worldwide. It occurs when the blood supply to a part of the brain is interrupted, resulting in the deprivation of oxygen and nutrients, leading to neuronal damage and...
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Advances in Distributional Reinforcement Learning: Bridging Theory with Algorithmic Practice
DownloadFall 2024
This thesis comprehensively investigates Distributional Reinforcement Learning~(RL), a vibrant research field that interplays between statistics and RL. As an extension of classical RL, distributional RL, on the one hand, embraces plenty of statistical ideas by incorporating distributional...
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Advances in Probabilistic Generative Models: Normalizing Flows, Multi-View Learning, and Linear Dynamical Systems
DownloadFall 2020
This thesis considers some aspects of generative models including my contributions in deep probabilistic generative architectures and linear dynamical systems. First, some advances in deep probabilistic generative models are contributed. Flow-based generative modelling is an emerging and highly...