Distilling Model Failures as Directions in Latent SpaceWe demonstrate how to distill patterns of model errors as directions in a latent space.
Uncovering Brittleness with DatamodelsIn the second part of our datamodels series, we use datamodels to identify and study a new form of model brittleness.
Missingness Bias in Model DebuggingWe demonstrate how current missingness approximations introduce biases into model debugging.
Predicting Predictions with DatamodelsIn the first part of our datamodels series, we introduce datamodeling and its (linear) instantiation on CIFAR-10.
Editing a ClassifierWe develop a methodology for directly editing the predictions rules of a pre-trained classifier with virtually no additional data collection.
Combining Diverse Feature PriorsWe explore how a diverse set of feature priors can be leveraged to improve model generalization.
Certified Patch Robustness Via Smoothed Vision Transformers (Part 2)We demonstrate how vision transformers lead to strong certified patch defenses with standard accuracy and inference times comparable to standard (non-robust) models.
Certified Patch Robustness Via Smoothed Vision Transformers (Part 1)We give an overview of smoothing-based defenses for certified robustness to adversarial attacks, and how it can be used to defend against adversarial patches.