16 citations · 16 across the 6 of their papers we have counts for
4 papers · 1 filter
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Koorosh Aslansefat, Mojgan Hashemian, Martin Walker +3
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning…
Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring
Al-Harith Farhad, Ioannis Sorokos, Andreas Schmidt +3
Machine Learning~(ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety…
StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures
Mohammed Naveed Akram, Akshatha Ambekar, Ioannis Sorokos +2
Reliability estimation of Machine Learning (ML) models is becoming a crucial subject. This is particularly the case when such \mbox{models} are deployed in safety-critical applicat…
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure
Koorosh Aslansefat, Ioannis Sorokos, Declan Whiting +2
Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditi…