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Skip to Search Results- 29Greiner, Russell (Computing Science)
- 14Schuurmans, Dale (Computing Science)
- 5Szepesvari, Csaba (Computing Science)
- 3Bowling, Michael (Computing Science)
- 1Bolduc, Francois (Pediatrics)
- 1Bowling, Mike (Computing Science)
- 13Machine learning
- 9Machine Learning
- 3Reinforcement Learning
- 2Diagnosis
- 2Fmri
- 2Functional magnetic resonance imaging
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Spring 2011
Standard survival analysis focuses on population-based studies. The objective of our work, survival prediction, is different: to find the most accurate model for predicting the survival times for each individual patient. We view this as a regression problem, where we try to map the features for...
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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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Fall 2020
While it is very difficult to diagnose/prognosis psychiatric disorders reliably, especially in early course, such early diagnosis/prognosis is critical for producing an effective treatment. This necessity has motivated many researchers to apply machine learning approaches to high-dimensional...
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Fall 2009
In this thesis we consider the task of catching a moving target with multiple pursuers, also known as the “Pursuit Game”, in which coordination among the pursuers is critical. Our testbed is inspired by the pursuit problem in video games, which require fast planning to guarantee fluid frame...
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Machine learning for medical applications with limited data: Incorporating domain expertise and addressing domain-shift
DownloadFall 2022
Machine learning has the potential to help medical experts to deliver better healthcare. There are, however, important technical challenges that need to be solved before we can develop reliable models for clinical practice, including: (1) Limited number of labeled instances, (2) Uncertainty of...
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Spring 2015
Graphical models use the intuitive and well-studied methods of graph theory to implicitly represent dependencies between variables in large systems. They can model the global behaviour of a complex system by specifying only local factors.This thesis studies inference in discrete graphical models...
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Fall 2012
This thesis provides a description of the cardiac rhythm as a latent chain of heart sound arrivals which occur over time, where each arrival generates a fixed window of observable data that can be described with arbitrary feature functions. This description of the process produces tractable...
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Spring 2014
Each patient with Type-1 diabetes must decide how much insulin to inject before each meal to maintain an acceptable level of blood glucose. The actual injection dose is based on a formula that takes current blood glucose level and the meal size into consideration. While following this insulin...
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Fall 2021
The optimization of non-convex objective functions is a topic of central interest in machine learning. Remarkably, it has recently been shown that simple gradient-based optimization can achieve globally optimal solutions in important non-convex problems that arise in machine learning, including...
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Spring 2013
Many machine learning algorithms learn from the data by capturing certain interesting characteristics. Decision trees are used in many classification tasks. In some circumstances, we only want to consider fixed-depth trees. Unfortunately, finding the optimal depth-d decision tree can require time...