| Fine-tuning can distort pretrained features and underperform out-of-distribution A Kumar, A Raghunathan, R Jones, T Ma, P Liang arXiv preprint arXiv:2202.10054, 2022 | 446 | 2022 |
| Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation K Shen, RM Jones, A Kumar, SM Xie, JZ HaoChen, T Ma, P Liang International conference on machine learning, 19847-19878, 2022 | 78 | 2022 |
| In-n-out: Pre-training and self-training using auxiliary information for out-of-distribution robustness SM Xie, A Kumar, R Jones, F Khani, T Ma, P Liang arXiv preprint arXiv:2012.04550, 2020 | 50 | 2020 |
| Featureless Deep Learning Methods for Automated Key-Term Extraction K Khosla, R Jones, N Bowman Stanford University, 2019 | 4 | 2019 |
| How does Contrastive Pre-training Connect Disparate Domains? K Shen, RM Jones, A Kumar, SM Xie, P Liang | 3 | 2021 |
| Stack overflow query outcome prediction R Jones, D Lin | 2 | 2016 |
| Temporal Link Prediction on the WikiLinkGraphs Dataset N Bowman, R Jones, S Shafi | 1 | |
| Extracting Kinematic Information Using Pose Estimation R Jones | | |
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
