Work by My Mentees
Predicting Out-of-Distribution Error with Confidence Optimal Transport
Yuzhe Lu, Zhenlin Wang, Runtian Zhai, Soheil Kolouri, Joseph Campbell, Katia P. Sycara
In ICLR 2023 Trustworthy ML Workshop
Paper   Code
We present a simple yet effective method to predict a model's OOD performance on an unknown distribution without any additional annotation. Our approach is rooted in the Optimal Transport theory, viewing test samples' output softmax scores from deep neural networks as empirical samples from an unknown distribution. We show that our method, Confidence Optimal Transport (COT), provides robust estimates of a model's performance on a target domain. Despite its simplicity, our method achieves state-of-the-art results on three benchmark datasets and outperforms existing methods by a large margin.