Decision making under uncertainty
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

Causal inference and discovery

A (relatively) recent addition to the arsenal of machine learning, causal inference is growing to become a powerful tool for the (phenomenological) understanding of natural and artificial phenomena. In particular, it provides means to …

Practical anomaly detection

Many industrial applications of automated decision-making involve the detection of anomalous behaviour. Precisely defining what this means is a complex problem hindered by strong class imbalances, lack of useful models or changing …

Bayesian ML and Probabilistic Programming

When the understanding of phenomena and interpretation of results are required in industrial or scientific applications, black-box models give way to hand-crafted (or automatically discovered) probabilistic models. Classical Bayesian …

Classifier calibration

For many applications of probabilistic classifiers it is important that the predicted confidence vectors reflect true probabilities (one says that the classifier is calibrated). Recently, it has been shown that common models fail to satisfy …

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