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Found 6 papers, 4 papers with code
Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates
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.
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning
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 prediction towards a desired target.
Conditional Generative Models for Counterfactual Explanations
Counterfactual instances offer human-interpretable insight into the local behaviour of machine learning models.
Monitoring and explainability of models in production
The machine learning lifecycle extends beyond the deployment stage.
Practical Bayesian Optimization of Objectives with Conditioning Variables
Bayesian optimization is a class of data efficient model based algorithms typically focused on global optimization.
Interpretable Counterfactual Explanations Guided by Prototypes
We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes.
