29 citations · 41 across the 4 of their papers we have counts for
8 papers
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.…
Practical Bayesian Optimization of Objectives with Conditioning Variables
Michael Pearce, Janis Klaise, Matthew Groves
Bayesian optimization is a class of data efficient model based algorithms typically focused on global optimization. We consider the more general case where a user is faced with mul…
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…