Explainability

Robust multimodal models have outlier features and encode more concepts

We investigate what makes multimodal models that show good robustness with respect to natural distribution shifts (e.g., zero-shot CLIP) different from models with lower robustness using interpretability.

Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar, Luca Zappella, Arno Blaas

Robust multimodal models have outlier features and encode more concepts

Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models

Over the past decade, Deep Learning (DL) models have proven to be efficient at classifying remotely sensed Earth Observation (EO) …

Arthur Vandenhoeke, Lennert Antson, Guillem Ballesteros, Jonathan Crabbé, Michal Shimoni

Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models

Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance

We assess the robustness of various interpretability methods by measuring how their explanations change when applying symmetries of the model to the input features.

Jonathan Crabbé, Mihaela van der Schaar

Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance

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

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

We benchmark treatment effect models with interpretability tools.

Jonathan Crabbé, Alicia Curth, 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

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

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

Interpretable Machine Learning

Creating an interface between machine learning models and human beings.

Interpretable Machine Learning