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Filtering by: Advisor Wong, Weng-Keen Remove constraint Advisor: Wong, Weng-Keen Contributor Li, Fuxin Remove constraint Contributor: Li, Fuxin Contributor Neal, Lawrence Remove constraint Contributor: Neal, Lawrence

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  • Characterizing and Addressing Dataset Shift with Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
    Resource Type:
    Dissertation
    Full Text:
    approved: Weng-Keen Wong This dissertation delves into understanding, characterizing, and addressing

  • Counterfactual States for Atari Agents via Generative Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
    Resource Type:
    Masters Thesis
    Full Text:
    States for Atari Agents via Generative Deep Learning Abstract approved: Weng-Keen Wong Although deep