Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"Trustworthy Machine Learning"”.
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Trustworthy machine learning throughout model’s life cycle
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Building trustworthy machine learning systems in adversarial environments
… particularly with the rise of big data and deep learning in the last decade, have greatly improved our daily life and at the same time created a long list of controversies. AI systems are often subject to malicious and stealthy subversion that jeopardizes their efficacy. Many of these issues stem …
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Trustworthy Machine Learning: From Algorithmic Transparency to Decision Support
Developing machine learning models worthy of decision-maker trust is crucial to using models in practice. Algorithmic transparency tools, such as explainability and uncertainty estimates, demonstrate the trustworthiness of a model to a decision-maker. In this thesis, we first explore how …
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EFFORTS TOWARD TRUSTWORTHY MACHINE LEARNING: MITIGATING OVERCONFIDENCE, HALLUCINATION, AND MODALITY BIAS
Trustworthy machine learning is critical for safe deployment of AI systems in high-stakes domains. Despite strong performance, models remain prone to reliability issues such as overconfidence, hallucinations, and modality bias. This thesis addresses these challenges through post-hoc methods and …
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Aligning AI with Human Values: A Path Towards Trustworthy Machine Learning Systems
Machine learning has become a powerful tool for harnessing vast amounts of data across diverse applications. However, as artificial intelligence (AI) technologies advance and become more deeply integrated into daily life, they also introduce risks such as malicious exploitation, misinformation, and …
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Convolutional Neural Networks for Robust Fynbos Leaf Classification: Enabling Trustworthy Machine Learning in Botanical Science
… Recognition Application (or FLORA) is a novel machine-learning tool created for the purposes of aiding conservation efforts of the Cape Floral Region, and the species of plants known as Fynbos in particular. Known for their distinctive evolutionary features, the species maintains a revered …
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Trustworthy Learning and Uncertainty Quantification under Constraints
Machine learning techniques have become increasingly important in a wide range of fields, including medicine, finance, and autonomous driving. While state-of-theart machine learning models can achieve promising prediction performance, there is an increasing need for reliable and trustworthy machine …
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Techniques for Interpretability and Transparency of Black-Box Models
The last decade witnessed immense progress in machine learning, which has been deployed in many domains such as healthcare, finance and justice. However, recent advances are largely powered by deep neural networks, whose opacity hinders people's ability to inspect these models. Furthermore, legal …
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Evolving Threats and Defenses in Machine Learning: Focus on Model Inversion and Beyond
Machine learning (ML) models are increasingly integrated into critical real-world applications, raising concerns about security, privacy, and trustworthiness. Among various emerging threats, model inversion (MI) attacks stand out due to their potential to compromise the confidentiality of training …
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A post-processing framework for group fairness
Machine learning models are increasingly powering automated decision-making systems that influence everyday life, thanks to their ease of deployment. But this convenience belies the risk that, without proper oversight, they may cause disparate impacts across demographic groups. For instance, models …
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Towards Trustworthy Learning in Temporal Learning Environments
This thesis explores how to build trustworthy machine learning systems that learn and adapt over time. As machine learning moves beyond static benchmarks and into real-world settings, where data distributions shift, environments change, and objectives evolve, it is more important and difficult to …
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ModelPred: A Framework for Predicting Trained Model from Training Data
… for building trust in various stages of a machine learning pipeline: from cleaning poor-quality samples and tracking important ones to be collected during data preparation, to calibrating uncertainty of model prediction, to interpreting why certain behaviors of a model emerge during …
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Certifiably trustworthy deep learning systems at scale
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Human factors in secure and non-abusive machine learning systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms