7 citations · 14 across the 5 of their papers we have counts for
5 papers
Towards Practicable Sequential Shift Detectors
Oliver Cobb, Arnaud Van Looveren
There is a growing awareness of the harmful effects of distribution shift on the performance of deployed machine learning models. Consequently, there is a growing interest in detec…
Context-Aware Drift Detection
Oliver Cobb, Arnaud Van Looveren
When monitoring machine learning systems, two-sample tests of homogeneity form the foundation upon which existing approaches to drift detection build. They are used to test for evi…
Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates
Oliver Cobb, Arnaud Van Looveren, Janis Klaise
Responding appropriately to the detections of a sequential change detector requires knowledge of the rate at which false positives occur in the absence of change. Setting detection…
Conditional Generative Models for Counterfactual Explanations
Arnaud Van Looveren, Janis Klaise, Giovanni Vacanti +1
Counterfactual instances offer human-interpretable insight into the local behaviour of machine learning models. We propose a general framework to generate sparse, in-distribution c…
Efficient Generalized Spherical CNNs
Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker +4
Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance…