Trustworthy and interpretable ML
Automating decisions based on data is a multifaceted problem with many pitfalls. We tackle multiple ends of the spectrum, from the detection of anomalies to calibration of models and Bayesian decision-making.

Content series in this area

Explainable AI

Large opaque models like neural networks require dedicated methods to study and interpret their behavior. In this series we review recent …

Trustworthy and Interpretable ML
17 items

Simulation-based Inference

Computer simulations provide high-fidelity models and allow to generate insights without the need of running real experiments. They are …

Trustworthy and Interpretable ML
7 items

Uncertainty Quantification

Uncertainty quantification (UQ) in machine learning is the practice of measuring or estimating uncertainty in models. It is a set of tools …

Trustworthy and Interpretable ML
6 items

Probabilistic Models

When the understanding of phenomena and interpretation of results are required in industrial or scientific applications, black-box models …

Trustworthy and Interpretable ML
13 items

Classifier calibration

For many applications of probabilistic classifiers it is important that the predicted confidence vectors reflect true probabilities (one …

Trustworthy and Interpretable ML
6 items
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