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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 15Szepesvari, Csaba (Computing Science)
- 4Schuurmans, Dale (Computing Science)
- 1Andras Gyorgy (Imperial College London, U.K.)
- 1Bowling, Michael (Computing Science)
- 1Greiner ,Russell (Computing Science)
- 1Greiner, Russell (Computing Science)
- 2Joulani, Pooria
- 1Abbasi-Yadkori, Yasin
- 1Afkanpour, Arash
- 1Aslan,Ozlem
- 1Balazs, Gabor
- 1Chandak, Kushagra
- 4Online Learning
- 3Learning theory
- 3Reinforcement Learning
- 2Machine Learning
- 2Machine learning
- 1Active learning
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Fall 2019
We study three problems in the application, design, and analysis of online optimization algorithms for machine learning. First, we consider speeding-up the common task of k-fold cross-validation of online algorithms, and provide TreeCV, an algorithm that reduces the time penalty of k-fold...
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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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Spring 2013
This work introduces the “online probing” problem: In each round, the learner is able to purchase the values of a subset of features for the current instance. After the learner uses this information to produce a prediction for this instance, it then has the option of paying for seeing the full...
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Spring 2023
Many practical problems in fields ranging from online advertising to genomics can be framed as the task of selecting the best option from among several choices, based on a limited number of noisy evaluations of the quality of each choice. Pure exploration in multi-armed bandits is an...
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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....