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 20 of 24 for “"domain generalization"”.
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Visual Domain Generalization via Self-Supervised Learning
L'abstract è presente nell'allegato / the abstract is in the attachment
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Efficient invariant feature subspace recovery for domain generalization
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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Domain generalization for sequential data via invariant subspace recovery
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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On the study of various methods for domain generalization and inverse problems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Federated Learning With Generalization To New Domains
… by proposing two novel approaches for federated domain generalization in both unsupervised and supervised settings. First, to tackle federated domain generalization in an unsupervised setting, we introduce Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients. …
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Evaluating the Role of Balanced Causal and Non-Causal Features in Predictive Modeling
… models that provides a stable performance across domains unseen during training, remains a persistent challenge. This generalizability of models is particularly challenging when the relationship between input features and target labels varies across domains. This study is motivated by recent work …
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Advancing Cross-Domain Fake News Detection: Enhanced Models to Improve Generalization and Tackle the Class Imbalance Problem
The rapid proliferation of fake news across domains, such as politics, health, and social media, poses a significant threat to the integrity of information dissemination, leading to misinformation that can affect public perception and decision-making. Detecting fake news is critical to preserving …
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Methods for Enhancing Robustness and Generalization in Machine Learning
… subgroup robustness and out of distribution generalization of machine learning models. First we introduce a formulation of Group DRO with soft group assignment. This formulation can be applied to data with noisy or uncertain group labels, or when only a small subset of the training data has …
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Towards Out-of-distribution Problem for Reinforcement Learning
… of dimensionality and poor out-of-distribution generalization of current probabilistic models. Current machine learning models requires data points to be independently identically distributed which is often not satisfied in real-world applications. This mismatch damages the direct application of …
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Exploring Knowledge Transfer with Deep Learning
… will mainly focus on transfer knowledge between domains by discussing the application of domain generalization. In this problem setup, we want to learn from multiple source domains to successfully classify data sampled from unseen target domains. I will present a methodology allowing each source …
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Advancing NLP Frontiers in Information Extraction, Opinion Mining, and Text Synthesis
… full-text keyphrase dataset, and propose domain-agnostic augmentation methods for low-resource settings. These advances form the foundation for the second pillar, which targets opinion mining through the introduction of the Target-Stance Extraction (TSE) task to jointly identify targets …
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Graph Representation Learning for Drug Discovery
… space. We address this challenge by a new domain generalization method called counterfactual consistency regularization, which seeks to eliminate spurious correlations in biological assays. Second, we extend property prediction capabilities to combinations of molecules, enabling us to …
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Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans
… heterogeneous adult and neonatal brain MRI domains. The research is conducted through three primary aims. First, the study evaluates the domain generalization of a 3D U-Net model across multi-center adult datasets - IXI, Calgary-Campinas, and OASIS-3, comprising 11 imaging centers with …
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Machine Learning Approaches that Extend Healthcare: Algorithms & Applications
… for practical imbalanced regression problems. • Domain Generalization: The thesis presents theoretically grounded learning methods that ensure generalization across imbalanced domains and unseen environments. • Subpopulation Shifts: The thesis studies learning in the presence of underrepresented …
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Efficient and Generalizable Machine Learning Models for Predicting Complex Dynamics
… high-capacity models that demonstrate impressive generalization typically require large and diverse training datasets, and may still struggle to generalize to aspects of the dynamics that are poorly represented in the training data. In this dissertation, we first show that reservoir computing—a …
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Towards externally valid machine learning: A spurious correlations perspective
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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Human action recognition in the real world: handling domain shift in open-set, source-free and multi-source scenarios
… to adapting models to visual and semantic domains potentially very different from those characterizing the data used to train them. Formally, this task goes by the name of Domain Adaptation (DA), and it has recently been devoted a significant amount of effort. While image-based content has …
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Physics-informed machine learning for smart decision-making in ultrasonic metal welding
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Domain Adaptation using Deep Adversarial Models
… Traditionally, data sets lie within the same domain and the same distribution is assumed for both training and testing sets. In many real-world scenarios such assumption would lead to very poor results, because data distribution may frequently be similar but not exactly identical. Sometimes, …
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