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 9 of 9 for “"reliable machine learning"”.
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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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Towards Rigorously Tested & Reliable Machine Learning for Health
When can we rely on machine learning in high-risk domains like healthcare? In the long-term, we want machine learning systems to be as reliable as any FDA-approved medication or diagnostic test. Building reliable models is complicated by the need for causal reasoning and robust performance. To …
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Operationalizing Reliable Machine Learning: From Data Collection to Model Presentation
Automated systems driven by machine learning (ML) have made exciting progress across a spectrum of applications. Despite such progress, encoded biases and other failure modes may create barriers to the real-world utility and reliability of such systems. For example, nonrandom data missingness, …
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Language-Centric Medical Image Understanding
… uses language prior to improve the robustness of learning algorithms under noisy supervision, and (3) a novel approach for calibrating linguistic expressions of diagnostic certainty, enabling more reliable communication of clinical findings. Together, these methods lead to more accurate, robust, …
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Spectral Analysis of Local Atomic Environments
… contributions strengthen the foundation for reliable machine learning models in computational materials science, advancing both the accuracy and efficiency of atomic-scale modeling for materials design and discovery.
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From Data, to Models, and Back: Making Machine Learning Predictably Reliable
Machine learning systems exhibit impressive performance, but we currently lack scalable ways to anticipate their successes, failure modes, and biases. This position limits our ability to deploy these systems in the appropriate contexts, and to build systems which we can confidently deploy in …
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Robust Machine Learning Methods in Solving Inverse Problems
… or ill-posed settings. Purely data-driven machine learning (ML) approaches have shown promising results by learning a direct mapping from measurements to ground-truth signals. While these methods often achieve superior reconstruction accuracy and faster runtime, they tend to lack …
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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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Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing
… metal AM. Thus, this work proposes three novel machine-learning methods for improving the quality of metal AM processes. These methods enable DTs to control quality in several processes, including laser powder bed fusion (LPBF) and additive friction stir deposition (AFSD). The proposed three …