Search Results for author:
Found 9 papers, 6 papers with code
Towards Practicable Sequential Shift Detectors
There is a growing awareness of the harmful effects of distribution shift on the performance of deployed machine learning models.
Desiderata for next generation of ML model serving
Inference is a significant part of ML software infrastructure.
Context-Aware Drift Detection
They are used to test for evidence that the distribution underlying recent deployment data differs from that underlying the historical reference data.
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.
Adversarial Detection and Correction by Matching Prediction Distributions
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 the Kullback-Leibler divergence between the classifier predictions on the original and reconstructed instances. The method is unsupervised, easy to train and does not require any knowledge about the underlying attack.
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.
