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Filtering by: Creator Meigs, Andrew J. Remove constraint Creator: Meigs, Andrew J. Advisor Fern, Alan Remove constraint Advisor: Fern, Alan Committee Member Wong, Weng-Keen Remove constraint Committee Member: Wong, Weng-Keen

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  • Revisiting output coding for sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this...
    Resource Type:
    Masters Thesis
    Full Text:
    unknown 9 distribution P (x, y), that is, ∆m(xi, yi) = [ − ∂ ∂Ψ(xi, yi) ∑ j `(xj, yj, Ψ(xj, yj

  • UCT for tactical assault battles in real-time strategy games

    Creator:
    Balla, Radha-Krishna
    Abstract:
    We consider the problem of tactical assault planning in real-time strategy games where a team of friendly agents must launch an assault on an enemy. This problem offers many challenges including a highly dynamic and uncertain environment, multiple agents, durative actions, numeric attributes, and different optimization objectives. While the dynamics...
    Resource Type:
    Masters Thesis

  • Integrating learning and search for structured prediction

    Creator:
    Doppa, Janardhan Rao
    Abstract:
    We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
    Resource Type:
    Dissertation
    Full Text:
    , Drew Bagnell, Roni Khardon, Andrew McCallum, Lise Getoor, Jason Eisner, Hal Daumé III, John Langford

  • Monte-Carlo planning for probabilistic domains

    Creator:
    Bjarnason, Ronald V.
    Abstract:
    This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyze how these algorithms can resolve some of the shortcomings of traditional probabilistic planning methods. The direct policy search...
    Resource Type:
    Dissertation
    Full Text:
    undetected in the relaxed domain, but detected as a special case pattern. After the 10♦ is built upon the J

  • Investigating Latent State and Uncertainty Representations in Reinforcement Learning

    Creator:
    Koul, Anurag
    Abstract:
    Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
    Resource Type:
    Dissertation