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Filtering by: Has journal SIAM Journal on Mathematical Analysis Remove constraint Has journal: SIAM Journal on Mathematical Analysis Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan Non-Academic Affiliation Oregon State University. Graduate School Remove constraint Non-Academic Affiliation: Oregon State University. Graduate School

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  • Bayesian optimization with empirical constraints

    Creator:
    Azimi, Javad
    Abstract:
    Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
    Resource Type:
    Dissertation
    Full Text:
    Azimi for the degree of Doctor of Philosophy in Computer Science presented on September 5, 2012. Title

  • Listing as a Potential Connection between Sets of Outcomes and Counting Processes

    Creator:
    Erickson, Sarah A.
    Abstract:
    Counting problems are rich in opportunities for students to make meaningful mathematical connections and develop non-algorithmic thinking; their accessible nature and applications to computer science make counting problems a valuable part of mathematics curricula. However, students struggle in various ways with counting, and while previous studies have indicated that listing...
    Resource Type:
    Masters Thesis
    Full Text:
    later study in which statistical analysis was done on the effects of systematic listing on student

  • Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

    Creator:
    Raghavan, Aswin
    Abstract:
    Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
    Resource Type:
    Dissertation
    Full Text:
    degree of Doctor of Philosophy in Computer Science presented on January 27, 2017. Title: Domain

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
    Resource Type:
    Dissertation
    Full Text:
    Doctor of Philosophy in Computer Science presented on July 21, 2009. Title: Efficient Training and

  • Applying hierarchical and adaptive control to coordinating simple robots

    Creator:
    Knudson, Matthew D.
    Abstract:
    Coordinating multiple robots to achieve a complex task requires solving two distinct control problems: the high-level control problem of ensuring that each robot aims to perform a useful task (e.g., coordination) and the low-level control problem of ensuring that each robot actually performs the correct actions to achieve its task...
    Resource Type:
    Masters Thesis
    Full Text:
    degree of Master of Science in Mechanical Engineering presented on February 11, 2008. Title: Applying

  • Learning MDP action models via discrete mixture trees

    Creator:
    Wynkoop, Michael S.
    Abstract:
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
    Resource Type:
    Masters Thesis
    Full Text:
    modeling trees in the tested domains. We performed an analysis of variance on each metric, holding the

  • Image features and learning algorithms for biological, generic and social object recognition

    Creator:
    Zhang, Wei
    Abstract:
    Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require...
    Resource Type:
    Dissertation
    Full Text:
    and Computer Engineering on March 16, 2009. Title: Image Features and Learning Algorithms for

  • Activity recognition in desktop environments

    Creator:
    Shen, Jianqiang
    Abstract:
    Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life. Intelligent desktop environments allow the desktop user to define a set of “activities” that characterize the user’s desktop work. These environments then attempt to identify...
    Resource Type:
    Dissertation
    Full Text:
    degree of Doctor of Philosophy in Computer Science presented on March 19, 2009. Title: Activity

  • Probabilities of voting paradoxes with three candidates

    Creator:
    McDonald, Leslie M.
    Abstract:
    Pardoxes in voting has been an interest of voting theorists since the 1800's when Condorcet demonstrated the key example of a voting paradox: voters with individually transitive rankings produce an election outcome which is not transitive. With Arrow's Impossibility Theorem, the hope of finding a fair voting method which accurately...
    Resource Type:
    Masters Thesis
    Full Text:
    the voters. Mathematical analysis of this question began during the French Revolution, most notably

  • Hierarchical structure discovery and transfer in sequential decision problems

    Creator:
    Mehta, Neville
    Abstract:
    Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or near-optimal decision-making, and transfer it to related problems instead of solving every problem in isolation. This dissertation makes three contributions toward this goal. The first...
    Resource Type:
    Dissertation
    Full Text:
    the degree of Doctor of Philosophy in Computer Science presented on September 23, 2011. Title