1k citations · 1.7k across the 13 of their papers we have counts for
3 papers · 1 filter
Predicting with Confidence on Unseen Distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi +2
Recent work has shown that the performance of machine learning models can vary substantially when models are evaluated on data drawn from a distribution that is close to but differ…
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
John Miller, Rohan Taori, Aditi Raghunathan +6
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments. In this paper, we empirically show that out-of-distri…
Contrasting Contrastive Self-Supervised Representation Learning Pipelines
Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt +2
In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning. Despite the success and adoption of representations learned through th…