2.3k citations · 2.8k across the 15 of their papers we have counts for
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In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
Sang Michael Xie, Ananya Kumar, Robbie Jones +3
Consider a prediction setting with few in-distribution labeled examples and many unlabeled examples both in- and out-of-distribution (OOD). The goal is to learn a model which perfo…
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
Sang Michael Xie, Tengyu Ma, Percy Liang
We focus on prediction problems with structured outputs that are subject to output validity constraints, e.g. pseudocode-to-code translation where the code must compile. While labe…
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang +2
Adversarial training augments the training set with perturbations to improve the robust error (over worst-case perturbations), but it often leads to an increase in the standard err…