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Skip to Search Results- 29Greiner, Russell (Computing Science)
- 21Bowling, Michael (Computing Science)
- 5Schuurmans, Dale (Computing Science)
- 2Szepesvari, Csaba (Computing Science)
- 2White, Martha (Computing Science)
- 1Bellemare, Marc (Google Brain)
- 2Wen, Junfeng
- 2White, Martha
- 1Afkanpour, Arash
- 1Allen, Felicity R
- 1Bastani, Meysam
- 1Behboudian, Paniz
- 12Machine Learning
- 11Machine learning
- 7Artificial Intelligence
- 7Reinforcement Learning
- 3Game Theory
- 2Abstractions
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Spring 2010
This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the form of metabolic pathways. A metabolic pathway describes a set of chemical reactions that...
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Spring 2010
In this work, we present a unified, general approach to variance reduction in agent evaluation using machine learning to minimize variance. Evaluating an agent's performance in a stochastic setting is necessary for agent development, scientific evaluation, and competitions. Traditionally,...
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Fall 2009
For zero-sum games, we have efficient solution techniques. Unfortunately, there are interesting games that are too large to solve. Here, a popular approach is to solve an abstract game that models the original game. We assume that more accurate the abstract games result in stronger strategies....
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Spring 2016
Monte Carlo methods are a simple, effective, and widely deployed way of approximating integrals that prove too challenging for deterministic approaches. This thesis presents a number of contributions to the field of adaptive Monte Carlo methods. That is, approaches that automatically adjust the...
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Spring 2015
Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that do not learn a value function and instead directly represent policy, often need fewer parameters to learn good policies....
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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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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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Advancing ECG Analysis through Machine Learning: A Study on Data Generation for ECG Classification and Feature Selection For Individual Survival Prediction
DownloadSpring 2024
Electrocardiograms (ECGs) are a valuable and easily-collected measurement of heart health, reflecting its morphology (R peak, QRS duration,..) and rhythm(sequence of multiple heartbeats). With the advance of machine learning, many studies utilize electrocardiogram (ECG) signals for various...
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Spring 2019
Forouzandehmoghadam, Amirhosein
A biomarker is a feature (e.g., gene expression, SNP, etc.) that is significantly different between two classes of instances – typically case and control. Knowing these biomarkers can help us understand a biological condition or identify the appropriate treatment for a certain disease. Many...
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Assessing the Feasibility of Learning Biomedical Phenotype Patterns Using High-Throughput Omics Profiles
DownloadSpring 2014
A decade after the completion of the human genome project, the rapid advancement of the high-throughput measurement technologies has made omics (genomics, epigenomics, transcriptomics, metabolomics) profiling feasible. The availability of such omics profiles has raised the hope for the...