no code implementations • 1 Apr 2022 • Kendrick Shen, Robbie Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen, Tengyu Ma, Percy Liang
We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e. g., photographs) and unlabeled data from a target domain (e. g., sketches) are used to learn a classifier for the target domain.
1 code implementation • ICLR 2022 • Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
no code implementations • 29 Sep 2021 • Kendrick Shen, Robbie Matthew Jones, Ananya Kumar, Sang Michael Xie, Percy Liang
We develop a conceptual model for contrastive learning under domain shifts, where data augmentations form connections between classes and domains that can be far apart.
no code implementations • ICLR 2021 • Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma
Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks.