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 20 of 87 for “"Machine Learning Systems"”.
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Provably reliable machine learning systems
Machine learning systems, which primarily use deep neural networks (DNNs), serve as critical components in safety-critical applications and compound AI systems. Despite their ubiquity, automated formal reasoning about their reliability has lagged significantly. Neural network verification is …
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Machine learning systems in constrained environments
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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On practical robustness of machine learning systems
… the importance of robustness in evaluating machine learning systems, an in particular systems involving deep learning. We consider these systems' vulnerability to adversarial examples--subtle, crafted perturbations to inputs which induce large change in output. We show that these adversarial …
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Towards Secure and Resilient Machine Learning Systems
Over the past decade, Machine Learning (ML) technologies have undergone revolutionary advancements, extending beyond traditional domains such as computer vision (CV) and natural language processing (NLP). One of the most significant breakthroughs is the development of transformer models, which …
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Towards Workload-aware Efficient Machine Learning Systems
Machine learning (ML) is transforming various aspects of our lives, driving the need for computing systems that efficiently support large-scale ML workloads. As models grow in size and complexity, existing systems struggle to adapt, limiting both performance and flexibility. Additionally, ML …
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Building trustworthy machine learning systems in adversarial environments
Modern AI systems, 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 …
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Understanding and mitigating privacy risk in machine learning systems
… years have witnessed a rapid development in machine learning systems and a widespread increase of machine learning applications. However, with the widespread adoption of machine learning, privacy issues have emerged. This thesis studies the privacy risk in modern machine learning systems in …
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Machine Learning Systems for Unsupervised Time Series Anomaly Detection
… contributions in the form of both algorithms and systems. First, it introduces three models that enlarge the design space of unsupervised time series anomaly detection: TadGAN, which leverages adversarial reconstruction; AER, which unifies predictive and reconstructive objectives in a single …
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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
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Multi-objective resource optimization for large scale machine learning systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Understanding and mitigating unintended demographic bias in machine learning systems
Machine Learning is becoming more and more influential in our society. Algorithms that learn from data are streamlining tasks in domains like employment, banking, education, heath care, social media, etc. Unfortunately, machine learning models are very susceptible to unintended bias, resulting in …
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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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Domain transferability, model robustness and data privacy in modern machine learning systems
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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Discovering trade-offs between fairness and accuracy in machine learning systems: a multi-objective approach
In recent years, the focus of machine learning has expanded from solely emphasizing accuracy to adopting a more comprehensive and human-centred perspective that includes privacy, fairness, and transparency. These aspects are frequently perceived as conflicting; for instance, there can be a …
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Measuring justice in machine learning
How can we build more just machine learning systems? To answer this question, we need to know both what justice is and how to tell whether one system is more or less just than another. That is, we need both a definition and a measure of justice. Theories of distributive justice hold that justice …
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A Systems Theory Approach to Cybersecuring a Supervised Machine Learning System
Machine learning is a rapidly growing field with many applications in areas such as healthcare, finance, and transportation. As machine learning becomes more prevalent, it is important to ensure that these systems are secure and can resist attacks from malicious actors. This is particularly …
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Improved methodology for evaluating adversarial robustness in deep neural networks
… to humans but can alter predictions of machine learning systems. Since the exact value of adversarial robustness is difficult to obtain for complex deep neural networks, accuracy of the models against perturbed examples generated by attack methods is empirically used as a proxy to …
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Learning with Constraint-Based Weak Supervision
Recent adaptations of machine learning models in many businesses has underscored the need for quality training data. Typically, training supervised machine learning systems involves using large amounts of human-annotated data. Labeling data is expensive and can be a limiting factor in using machine …
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Advancing Equity and Reliability in Machine Learning
… have serious consequences for the behavior of machine learning models across environments and demographic subgroups. If a disease is systematically underdiagnosed, machine learning models trained on this data risk replicating patterns of underdiagnosis. If the data used to evaluate machine …
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Machine learning ecosystem : implications for business strategy centered on machine learning
As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine …
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