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Skip to Search Results- 14Schuurmans, Dale (Computing Science)
- 4Szepesvari, Csaba (Computing Science)
- 3Bowling, Michael (Computing Science)
- 2Greiner, Russell (Computing Science)
- 1Bowling, Mike (Computing Science)
- 1Müller, Martin (Computing Science)
- 5Machine learning
- 3Reinforcement Learning
- 2Machine Learning
- 1Abstractions
- 1Agent evaluation
- 1Artificial intelligence
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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 2015
This dissertation explores regularized factor models as a simple unification of machine learn- ing problems, with a focus on algorithmic development within this known formalism. The main contributions are (1) the development of generic, efficient algorithms for a subclass of regularized...
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Spring 2017
Co-embedding is the process of mapping elements from multiple sets into a common latent space, which can be exploited to infer element-wise associations by considering the geometric proximity of their embeddings. Such an approach underlies the state of the art for link prediction, relation...
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Spring 2021
This dissertation demonstrates how to utilize data collected previously from different sources to facilitate learning and inference for a target task. Learning from scratch for a target task or environment can be expensive and time-consuming. To address this problem, we make three contributions...