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- 18Machine Learning
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- 83Graduate and Postdoctoral Studies (GPS), Faculty of
- 83Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 5Computing Science, Department of
- 5Computing Science, Department of/Technical Reports (Computing Science)
- 2Chemical and Materials Engineering, Department of
- 2Chemical and Materials Engineering, Department of/Process Systems Engineering
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Spring 2013
In a discrete-time online control problem, a learner makes an effort to control the state of an initially unknown environment so as to minimize the sum of the losses he suffers, where the losses are assumed to depend on the individual state-transitions. Various models of control problems have...
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Fall 2021
Climate change concerns have raised awareness about the importance of decarbonizing the power sector. In achieving such a goal, energy storage is a critical operation that is currently done using mostly fossil fuels as chemical energy storage. The only viable alternative is battery energy storage...
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Spring 2015
This thesis consists of two independent projects, each contributing to a central goal of artificial intelligence research: to build computer systems that are capable of performing tasks and solving problems without problem-specific direction from us, their designers. I focus on two formal...
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Fall 2023
Krishna Guruvayur Sasikumar, Aakash
The application of reinforcement learning (RL) to the optimal control of building systems has gained traction in recent years as it can reduce building energy consumption and improve human comfort, without requiring the knowledge of the building model. However, existing RL solutions for building...
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Fall 2018
Knowledge is central to intelligence. Intelligence can be thought of as the ability to acquire knowledge and apply it effectively. Despite being a subject of intense interest in artificial intelligence, it is not yet clear what the best approach is for an intelligent system to acquire and...
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Fall 2020
Language Modeling (LM) is often formulated as a next-word prediction problem over a large vocabulary, which makes it challenging. To effectively perform the task of next-word prediction, Long Short Term Memory networks (LSTMs) must keep track of many types of information. Some information is...
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Fall 2023
Many real-world tasks in fields such as robotics and control can be formulated as constrained Markov decision processes (CMDPs). In CMDPs, the objective is usually to optimize the return while ensuring some constraints being satisfied at the same time. The primal-dual approach is a common...
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Fall 2020
Computing a Nash equilibrium in zero-sum games, or more generally saddle point optimization, is a fundamental problem in game theory and machine learning, with applications spanning across a wide variety of domains, from generative modeling and computer vision to super-human AI in imperfect...
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Fall 2011
This thesis studies the reinforcement learning and planning problems that are modeled by a discounted Markov Decision Process (MDP) with a large state space and finite action space. We follow the value-based approach in which a function approximator is used to estimate the optimal value function....