Robustness

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

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

Joint Training of Deep Ensembles Fails Due to Learner Collusion

Training deep ensembles via a shared objective results in degenerate behavior.

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

Joint Training of Deep Ensembles Fails Due to Learner Collusion

Robust Machine Learning

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

Robust Machine Learning