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- 4Exploration
- 2Reinforcement Learning
- 1Aboriginal identification
- 1Artificial Intelligence
- 1Atari 2600
- 1Chen, Haolan
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- 1Kumaraswamy, Raksha K
- 1Montague, John J
- 1Munshi, Amr
- 1Wong, Kai On
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Spring 2020
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
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Fall 2019
The traditional electric grid based on centralized generation plants and unidirectional transmission and distribution systems is transitioning to a smart grid that is decentralized and multidirectional with high integration of information and communication technologies. With the rapid development...
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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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Public Health Applications Using Big Data and Machine Learning Methods: Name- and Location-based Aboriginal Ethnicity Classification and Sentiment Analysis of Breast Cancer Screening in the United States Using Twitter
DownloadFall 2017
Applications using big data and machine learning techniques are transforming how people live in the 21st century, however they are generally underutilized in public health compared to other domains. We proposed and conducted two independent studies to investigate how big data and machine learning...
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Sample-Efficient Control with Directed Exploration in Discounted MDPs Under Linear Function Approximation
DownloadSpring 2022
An important goal of online reinforcement learning algorithms is efficient data collection to learn near-optimal behaviour, that is, optimizing the exploration-exploitation trade-off to reduce the sample-complexity of learning. To improve sample-complexity of learning it is essential that the...
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Separating-Plane Factorization Models: Scalable Recommendation from One-class Implicit Feedback
DownloadFall 2016
We study the large-scale video recommendation problem based on user viewing logs instead of explicit ratings. As viewing records are implicitly positive samples, existing matrix factorization methods fail to generate discriminative recommendations based on such one-class data. We propose a...
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Fall 2016
Big data applications demand and consequently lead to developments of diverse scalable data management systems, ranging from NoSQL systems to the emerging NewSQL systems. In order to serve thousands of applications and their huge amounts of data, data management systems must be capable of...
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Spring 2016
How can the principles and concepts applied by visual communication designers be used to assist in exploring and understanding the massive, complex volumes of data now available to Digital Humanities researchers? One method we might employ to help us more easily comprehend the implications of...