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Skip to Search Results- 5Machine Learning
- 3Reinforcement Learning
- 2Artificial Intelligence
- 1Domain Adaptation
- 1Emergent Communication
- 1Monte Carlo
- 3Bowling, Michael (Computing Science)
- 2Schuurmans, Dale (Computing Science)
- 1Greiner, Russell (Computing Science)
- 1Schuurmans, Dale (Computing Science)
- 1Schuurmans,Dale (Computing Science)
- 1Taylor, Matthew (Computing Science)
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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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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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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 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...
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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...