29 citations · 58 across the 7 of their papers we have counts for
8 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…
Desiderata for next generation of ML model serving
Sherif Akoush, Andrei Paleyes, Arnaud Van Looveren +1
Inference is a significant part of ML software infrastructure. Despite the variety of inference frameworks available, the field as a whole can be considered in its early days. This…
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…
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning
Robert-Florian Samoilescu, Arnaud Van Looveren, Janis Klaise
Counterfactual instances are a powerful tool to obtain valuable insights into automated decision processes, describing the necessary minimal changes in the input space to alter the…
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…
Monitoring and explainability of models in production
Janis Klaise, Arnaud Van Looveren, Clive Cox +2
The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services.…
Adversarial Detection and Correction by Matching Prediction Distributions
Giovanni Vacanti, Arnaud Van Looveren
We present a novel adversarial detection and correction method for machine learning classifiers.The detector consists of an autoencoder trained with a custom loss function based on…
Interpretable Counterfactual Explanations Guided by Prototypes
Arnaud Van Looveren, Janis Klaise
We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes. We show that class prototypes, o…