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 …

Abstract Machine learning models frequently experience performance drops under
distribution shifts. The underlying cause of such shifts may be multiple simultaneous factors …

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 …

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 …

Despite the increasing success of Machine Learning (ML) techniques in real-world
applications, their maintenance over time remains challenging. In particular, the prediction …

Machine learning (ML) solutions are increasingly entering healthcare. They are complex,
sociotechnical systems that include data inputs, ML models, technical infrastructure and …

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.
However, existing uncertainty estimation approaches rely on low-dimensional distributional …

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 …

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 …

The industry increasingly relies on deep learning (DL) technology for manufacturing
inspections, which are challenging to automate with rule-based machine vision algorithms …