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Filtering by: Creator Reitz, Stuart R. Remove constraint Creator: Reitz, Stuart R. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Shrunken learning rates do not improve AdaBoost on benchmark datasets

    Creator:
    Forrest, Daniel L. K.
    Abstract:
    Recent work has shown that AdaBoost can be viewed as an algorithm that maximizes the margin on the training data via functional gradient descent. Under this interpretation, the weight computed by AdaBoost, for each hypothesis generated, can be viewed as a step size parameter in a gradient descent search. Friedman...
    Resource Type:
    Masters Thesis
    Full Text:
    problems that fit into this framework depending on the values of y. If y E R, the problem is called

  • Development of a dose conversion factor from uptakes of Po-210 through smoking using a modified ICRP 30 lung model

    Creator:
    Hout, Jason D.
    Abstract:
    A thesis presented on the determination of factors related to deposition, clearance, and dosimetry of the ICRP 30 lung model as related to smoking. Variations in calculations used to determine radiation doses leave a need to determine a standard method to determine absorbed dose from smoking. Standard parameters are used...
    Resource Type:
    Masters Thesis
    Full Text:
    a frequency of 2 puffs/min. TGLD uses a 15 breath/min frequency, and data shown by Stuart (Stuart

  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
    Resource Type:
    Dissertation
    Full Text:
    current program location `′ is at choice (A′, a′). The last choice is (A, a) with reward r seen in

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
    Resource Type:
    Dissertation

  • Learning and Improving Policies for Probabilistic Planning Problems

    Creator:
    Issakkimuthu, Murugeswari
    Abstract:
    In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4 FC-DRP vs S-DRP vs R-DRP Results . . . . . . . . . . . . . . . . . . . . 19 3.1 Game-of-Life

  • Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty

    Creator:
    Marcon dos Santos, Gilberto
    Abstract:
    This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost...
    Resource Type:
    Dissertation

  • Interpretable Machine Learning: Applications in Biology and Genomics

    Creator:
    Danaee, Padideh
    Abstract:
    Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
    Resource Type:
    Dissertation

  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
    Resource Type:
    Dissertation
    Full Text:
    labeled constraints and correctly labeled constraints used in the comparison of Figure 3.9. “R”: relative

  • Variational Satisfiability Solving

    Creator:
    Young, Jeffrey M.
    Abstract:
    Over the last two decades, satisfiability and satisfiability-modulo theory (SAT/SMT) solvers have grown powerful enough to be general purpose reasoning engines throughout software engineering and computer science. However, most practical use cases of SAT/SMT solvers require not just solving a single SAT/SMT problem, but solving sets of related SAT/SMT problems....
    Resource Type:
    Dissertation
    Full Text:
    ∧ c ∧ e q = a ∧ (b ∨ ¬i) ∧ c ∧ (g → c) r = z ↔ (a ∧ b ∧ c ∧ e) 2By the standard, this is a well

  • Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

    Creator:
    Dodge, Jonathan E.
    Abstract:
    Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
    Resource Type:
    Dissertation