Deep learning

Concept Activation Regions: A Generalized Framework For Concept-Based Explanations

We extend existing feature and example importance methods to unsupervised learning.

Jonathan Crabbé, Mihaela van der Schaar

Concept Activation Regions: A Generalized Framework For Concept-Based Explanations

Label-Free Explainability for Unsupervised Models

We extend existing feature and example importance methods to unsupervised learning.

Jonathan Crabbé, Mihaela van der Schaar

Label-Free Explainability for Unsupervised Models

Representation Learning

Learning realistic representations of the world.

Representation Learning

Robust Machine Learning

Making sure that machines are doing what they are supposed to do.

Robust Machine Learning

Explaining Latent Representations with a Corpus of Examples

We introduce SimplEx, a case-based reasoning explanation method that permits to decompose latent representations with a corpus of example.

Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

Explaining Latent Representations with a Corpus of Examples

Explaining Time Series Predictions with Dynamic Masks

We introduce Dynamask, a perturbation-based feature importance method to explain the predictions of time series models.

Jonathan Crabbé, Mihaela van der Schaar

Explaining Time Series Predictions with Dynamic Masks

Learning outside the black-box: the pursuit of interpretable models

We introduce Symbolic Pursuit, a new method for symbolic regression based on Meijer G-functions and the projection pursuit algorithm.

Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar

Explainable Artificial Intelligence

Creating an interface between machine learning models and human beings.

Explainable Artificial Intelligence