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Skip to Search Results- 29Greiner, Russell (Computing Science)
- 21Bowling, Michael (Computing Science)
- 14Schuurmans, Dale (Computing Science)
- 6Szepesvari, Csaba (Computing Science)
- 2White, Martha (Computing Science)
- 1Bellemare, Marc (Google Brain)
- 14Machine learning
- 13Machine Learning
- 9Reinforcement Learning
- 7Artificial Intelligence
- 3Game Theory
- 2Abstractions
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Fall 2012
Functional Magnetic Resonance Imaging (fMRI) measures the dynamic activity of each voxel of a brain. This dissertation addresses the challenge of learning a diagnostic classifier that uses a subject’s fMRI data to distinguish subjects with neuropsychiatric disorders from healthy controls. fMRI...
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Fall 2019
Artificial agents have been shown to learn to communicate when needed to complete a cooperative task. Some level of language structure (e.g., compositionality) has been found in the learned communication protocols. This observed structure is often the result of specific environmental pressures...
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Spring 2019
In the reinforcement learning (RL) problem an agent must learn how to act optimally through trial-and-error interactions with a complex, unknown, stochastic environment. The actions taken by the agent influence not just the immediate reward it observes but also the future states and rewards it...
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Fall 2013
This thesis presents new algorithms for dealing with large scale reinforcement learning problems. Central to this work is the Atari 2600 platform, which acts as both a rich evaluation framework and a source of challenges for existing reinforcement learning methods. Three contributions are...
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Hindsight Rational Learning for Sequential Decision-Making: Foundations and Experimental Applications
DownloadFall 2022
This thesis develops foundations for the development of dependable, scalable reinforcement learning algorithms with strong connections to game theory. I present a version of rationality for learning---one grounded in the learner's experience and connected with the rationality concepts of...
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Spring 2020
Mapping the macrostructural connectivity of the living human brain is one of the primary goals of neuroscientists who study connectomics. The reconstruction of a brain's structural connectivity, aka its connectome, typically involves applying expert analysis to diffusion-weighted magnetic...
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Fall 2017
On the one hand, theoretical analyses of machine learning algorithms are typically performed based on various probabilistic assumptions about the data. While these probabilistic assumptions are important in the analyses, it is debatable whether such assumptions actually hold in practice. Another...
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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...