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 12 of 12 for “"Covariate Shift"”.
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Addressing two issues in machine learning : interpretability and dataset shift
… a shortcoming of a popular approach to handling covariate shift, in which the training distribution and that for which predictions need to be made have different covariate distributions. In particular, the existing importance weighting approach to handling covariate shift suffers from high …
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One-pass algorithms for large and shifting data sets
… the machine learning community, is distribution shift, which occurs naturally in many problem domains such as intrusion detection and EEG signal mapping in the Brain-Computer Interface domain. This means that the i.i.d. assumption between the training and test data does not hold, causing …
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Towards AI Safety via Interpretability and Oversight
… data scarcity, class imbalance, label noise, and covariate shift. While SAEs occasionally outperform baseline methods, they fail to consistently enhance task performance, underscoring a potentially critical limitation of SAEs. Lastly, we introduce a quantitative framework to evaluate scalable …
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Learning under differing training and test distributions
… multi-task learning and learning under covariate shift and sample selection bias. Several new models are derived that directly characterize the divergence between training and test distributions, without the intermediate step of estimating training and test distributions separately. The …
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Novel Methods for Extending Causal Inference to a Target Population
… and sampling scores, to improve estimation under covariate shift. To address positivity violations, a smooth inclusion weight is introduced to restrict inference to the well-represented subpopulation. The framework incorporates flexible machine learning tools to enable valid and efficient …
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Machine learning models for reliable airline ancillary pricing
… drop during the COVID-19 pandemic, we study covariate shift to examine changes in the underlying data. We focus on shift detection before and during COVID-19 through a blend of (i) discriminative model training to distinguish train from test, and (ii) statistical testing of estimated feature …
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Prediction games : machine learning in the presence of an adversary
… this scenario reduces to learning under covariate shift. We derive a new integrated as well as a two-stage method to account for this data set shift. In case studies on email spam filtering we empirically explore properties of all derived models as well as several existing baseline …
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Robot See, Robot Do: On the Development of Robust and Adaptive Imitation Learning for Robots
… imitation learning algorithms are often prone to covariate shift when they encounter data not seen during training. To tackle this challenge, we develop Stable Behavior Cloning (Stable-BC), a stability-driven imitation learning algorithm. This algorithm ensures that robots maintain reliable …
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Dynamic pricing of airline ancillaries under distribution shift
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01
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Robust data-driven optimization for dynamic and decision-dependent systems under uncertainty
… biased) policies. In these settings, only the covariates and outcomes of selected individuals are observed, while the performance of those screened out remains unknown. This creates selection bias and covariate shift that complicate learning, and fairness concerns arise because historical …
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Rethinking Machine Learning for Heterogeneous Treatment Effect Estimation
… of interest, this leads to additional covariate shifts that need to be accounted for and investigate strategies for doing so. Overall, we highlight that while the machine learning literature on heterogeneous treatment effect estimation has focused almost exclusively on tackling the …
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Towards robust and domain invariant feature representations in Deep Learning
… representations that are invariant to external covariate shift, which is more commonly termed as domain shift. Towards learning representations robust to external nuisance factors, we propose an approach that couples a deep convolutional neural network with a low-dimensional discriminative …