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Filtering by: Creator Sobrino, Jose Manuel Remove constraint Creator: Sobrino, Jose Manuel Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Representationally Robust and Scalable Learning over Relational Databases

    Creator:
    Picado Leiva, Jose Manuel
    Abstract:
    Learning novel concepts from relational databases is an important problem with applications in several disciplines, such as data management, natural language processing, and bioinformatics. For a learning algorithm to be effective, the input data should be clean and in some desired representation. However, real-world data is usually heterogeneous – the...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Jose Manuel Picado Leiva

  • Open Set Learning with Counterfactual Images

    Creator:
    Neal, Lawrence W.
    Abstract:
    In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
    Resource Type:
    Masters Thesis
    Full Text:
    Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz

  • Enhancing Search-Based Techniques with Information Control Dependencies

    Creator:
    Aburas, Ali
    Abstract:
    Software testing is a very important task during software development and it can be used to improve the quality and reliability of the software system. One potential way to reduce the cost and increase the efficiency of software testing is to generate test data automatically. Search-based approaches successfully generate unit...
    Resource Type:
    Dissertation

  • Statistical Properties and Guarantees for Certain Anomaly Detection Relevant Problems

    Creator:
    Liu, Si
    Abstract:
    Anomaly detection aims at detecting the points that appear different than the majority of the data, such that they are suspected to be generated from a different distribution. Anomaly detectors have been applied in many different fields, such as detecting fraudulent behaviors in bank transaction, finding broken sensors in a...
    Resource Type:
    Dissertation