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.
Results
Showing 1 to 10 of 10 for “"Out-of-Distribution Data"”.
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EFFORTS TOWARD TRUSTWORTHY MACHINE LEARNING: MITIGATING OVERCONFIDENCE, HALLUCINATION, AND MODALITY BIAS
… 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 targeted …
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Last Layer Retraining of Selectively Sampled Wild Data Improves Performance
… perform well in labs where training and testing data are in a similar domain, they experience significant drops in performance in the wild where the data can lie in domains outside the training distribution. Out-of-distribution (OOD) generalization is difficult because these domains are …
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Towards Out-of-distribution Problem for Reinforcement Learning
… high-quality models require a large amount of data, parameters as well as computation power. This originates from the curse of dimensionality and poor out-of-distribution generalization of current probabilistic models. Current machine learning models requires data points to be independently …
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Uncertainty-aware learning from sparse, unlabelled, and out-of-distribution time series
… learning is increasingly popular in the analysis of healthcare time series, as it can support improved diagnostics, personalised monitoring, and effective performance tracking. The ever-increasing availability of datasets from wearable sensors, mobile devices, and continuous monitoring …
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SAFEGUARDING AI SYSTEMS AGAINST UNEXPECTED INPUTS
… achieved remarkable success across a broad range of applications. However, perturbations such as natural image corruptions or crafted malicious queries, can cause significant performance degradation. This poses severe risks in safety-critical applications, such as autonomous driving and clinical …
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Heterogeneous machine learning with decentralized data
… machine learning with decentralized data, where multiple clients with distinct data distributions jointly train or adapt machine learning models under the coordination of a central server. Throughout the process, clients’ private data never leave their local devices. This paradigm …
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AI-driven Automated Medical Imaging Analysis
… by revealing the internal structure of the human body at normal anatomical and physiological levels. Manual analyzing medical images demands attention and is time-consuming, requiring well-trained expertise. The speed, fatigue, and experience may limit the diagnostic performance, …
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Optimizing Decision-Making under Uncertainty -- A Data-Driven Perspective
… processes are fundamental to many aspects of daily life, from allocating educational resources and optimizing logistics routes to scheduling renewable energy generation and distributing vaccines. These complex problems are typically framed as mathematical optimization problems, where …
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Epistemic deep learning : enabling machine learning models to ‘know when they do not know’
… and unreliable predictions when models encounter out-of-distribution data, adversarial perturbations, or naturally fluctuating environments. This thesis, titled Epistemic Deep Learning: Enabling Machine Learning Models to ‘Know When They Do Not Know’, addresses these critical challenges by …
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Advances in Meta-Learning, Robustness, and Second-Order Optimisation in Deep Learning
… to learn, that is, to accumulate knowledge about how to do a task without having been programmed specifically for that purpose. In this thesis, we are concerned with learning from two different perspectives: domains to which we may apply efficient machine learners and ways in which we can …