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Subject: Machine Learning Supervisors: Szepesvari, Csaba (Computing Science)

  • Probe-Efficient Learning

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    Spring 2013

    Zolghadr, Navid

    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...

  • Regularization in reinforcement learning

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    Fall 2011

    Farahmand, Amir-massoud

    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....

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