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Adaptive uncertainty estimation via high-dimensional testing on latent representations
Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.
However, existing uncertainty estimation approaches rely on low-dimensional distributional …
However, existing uncertainty estimation approaches rely on low-dimensional distributional …
A unified framework for dataset shift diagnostics
Supervised learning techniques typically assume training data originates from the target
population. Yet, in reality, dataset shift frequently arises, which, if not adequately taken into …
population. Yet, in reality, dataset shift frequently arises, which, if not adequately taken into …
Efficiently mitigating the impact of data drift on machine learning pipelines
Despite the increasing success of Machine Learning (ML) techniques in real-world
applications, their maintenance over time remains challenging. In particular, the prediction …
applications, their maintenance over time remains challenging. In particular, the prediction …
" why did the model fail?": Attributing model performance changes to distribution shifts
H Zhang, H Singh, M Ghassemi, S Joshi - 2023 - proceedings.mlr.press
Abstract Machine learning models frequently experience performance drops under
distribution shifts. The underlying cause of such shifts may be multiple simultaneous factors …
distribution shifts. The underlying cause of such shifts may be multiple simultaneous factors …
Empirical data drift detection experiments on real-world medical imaging data
While it is common to monitor deployed clinical artificial intelligence (AI) models for
performance degradation, it is less common for the input data to be monitored for data drift …
performance degradation, it is less common for the input data to be monitored for data drift …
Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives
Machine learning (ML) solutions are increasingly entering healthcare. They are complex,
sociotechnical systems that include data inputs, ML models, technical infrastructure and …
sociotechnical systems that include data inputs, ML models, technical infrastructure and …
Out of the ordinary: Spectrally adapting regression for covariate shift
Designing deep neural network classifiers that perform robustly on distributions differing
from the available training data is an active area of machine learning research. However, out …
from the available training data is an active area of machine learning research. However, out …
Feature attribution explanation to detect harmful dataset shift
Detecting whether a distribution shift has occurred in the dataset is a critical aspect when
implementing machine learning models, as even a small shift in the data distribution may …
implementing machine learning models, as even a small shift in the data distribution may …
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
We derive a new,(almost) guaranteed upper bound on the error of deep neural networks
under distribution shift using unlabeled test data. Prior methods are either vacuous in …
under distribution shift using unlabeled test data. Prior methods are either vacuous in …
A Geometric Explanation of the Likelihood OOD Detection Paradox
Likelihood-based deep generative models (DGMs) commonly exhibit a puzzling behaviour:
when trained on a relatively complex dataset, they assign higher likelihood values to out-of …
when trained on a relatively complex dataset, they assign higher likelihood values to out-of …