Latent Representation

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization

We introduce TANGOS, a regularization method that orthogonalizes the gradient attribution of neurons to improve the generalization of deep neural networks on tabular data.

Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar

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

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data

We introduce Data-IQ, a data-centric framework to identify ambiguous examples.

Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar

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

Data-SUITE: Data-centric identification of in-distribution incongruous examples

We introduce Data-SUITE, a data-centric framework to identify incongruous examples.

Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar

Latent Density Models for Uncertainty Categorization

We introduce DAUX, an interpretability framework to interpret model uncertainty.

Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar

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