---
res:
  bibo_abstract:
  - "As neural-network-based models grow both in size and popularity, interest has
    grown in making the models smaller and more efficient to train. To that end, many
    methods have been proposed to prune models by reducing their number of nonzero
    parameters. Additionally, parameter-efficient fine-tuning, in which a much smaller
    number of parameters than the total contained in the model is updated during training,
    has become very popular, especially in the space of Large Language Models. At
    the same time, the increasingly routine deployment of machine learning in real-world
    applications has spurred a drive to make them more trustworthy - in the sense
    of, among other things, being unbiased, interpretable, and editable. In this thesis,
    we examine the interplay between efficiency and trustworthiness.\r\n\r\nFirst,
    we analyze the effects of model pruning on bias in computer vision models, demonstrating
    that increased sparsity leads to greater bias, largely as a function of increased
    model uncertainty in marginal cases. Based on this observation, we propose several
    bias mitigation techniques. Then, we demonstrate that example-specific model pruning
    can improve model interpretation methods while improving pruning efficiency to
    make example-specific model pruning feasible in real time. Then, we investigate
    the effectiveness of parameter-efficient and data-efficient model personalization
    via fine-tuning, demonstrating that it is highly feasible with very small computational
    and data resources. Finally, we consider efficiency in editing model knowledge
    using a custom synthetic data framework, demonstrating that parameter-efficient,
    low-rank fine-tuning frequently outperforms full-rank fine-tuning, and, additionally,
    that restricting which model blocks are fine-tuned frequently improves results.
    Together, the results in this thesis provide new insights and techniques for combining
    trustworthiness and efficiency during neural network inference and training.\r\n\r\n-----------------“In
    reference to IEEE copyrighted material which is used with permission in this thesis,
    the IEEE does not endorse any of [name of university or educational entity]’s
    products or services. Internal or personal use of this material is permitted.
    If interested in reprinting/republishing IEEE copyrighted material for advertising
    or promotional purposes or for creating new collective works for resale or redistribution,
    please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html
    to learn how to obtain a License from RightsLink. If applicable, University Microfilms
    and/or ProQuest Library, or the Archives of Canada may supply single copies of
    the dissertation.”@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Eugenia B
      foaf_name: Iofinova, Eugenia B
      foaf_surname: Iofinova
      foaf_workInfoHomepage: http://www.librecat.org/personId=f9a17499-f6e0-11ea-865d-fdf9a3f77117
    orcid: 0000-0002-7778-3221
  bibo_doi: 10.15479/AT-ISTA-21854
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2663-337X
  dct_language: eng
  dct_publisher: Institute of Science and Technology Austria@
  dct_title: On the utility and effects of efficiency in artificial neural networks@
...