| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4802 | 2021 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... Transactions on Machine Learning Research (TMLR), 2023 | 1217 | 2023 |
| Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution A Kumar, A Raghunathan, R Jones, T Ma, P Liang International Conference on Learning Representations (ICLR), 2022 | 735 | 2022 |
| Verified Uncertainty Calibration A Kumar, P Liang, T Ma Neural Information Processing Systems (NeurIPS), 2019 | 418 | 2019 |
| Understanding Self-Training for Gradual Domain Adaptation A Kumar, T Ma, P Liang International Conference on Machine Learning (ICML), 2020 | 263 | 2020 |
| Gpt-4o system card A Hurst, A Lerer, AP Goucher, A Perelman, A Ramesh, A Clark, AJ Ostrow, ... arXiv preprint arXiv:2410.21276, 2024 | 219 | 2024 |
| Surgical fine-tuning improves adaptation to distribution shifts Y Lee, AS Chen, F Tajwar, A Kumar, H Yao, P Liang, C Finn International Conference on Learning Representations (ICLR), 2023 | 208 | 2023 |
| Finetune like you pretrain: Improved finetuning of zero-shot vision models S Goyal, A Kumar, S Garg, Z Kolter, A Raghunathan Conference on Computer Vision and Pattern Recognition (CVPR), 2023 | 147 | 2023 |
| Extending the wilds benchmark for unsupervised adaptation S Sagawa*, PW Koh*, T Lee*, I Gao*, SM Xie, K Shen, A Kumar, W Hu, ... International Conference on Learning Representations (ICLR), 2022 | 136 | 2022 |
| Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation K Shen*, R Jones*, A Kumar*, SM Xie*, JZ HaoChen, T Ma, P Liang International Conference on Machine Learning (ICML), 2022 | 116 | 2022 |
| Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? R Bommasani, K Creel, A Kumar, D Jurafsky, P Liang Advances in Neural Information Processing Systems (NeurIPS), 2022 | 91 | 2022 |
| Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures J Uesato*, A Kumar*, C Szepesvari*, T Erez, A Ruderman, K Anderson, ... International Conference on Learning Representations (ICLR), 2019 | 89 | 2019 |
| Self-training avoids using spurious features under domain shift Y Chen*, C Wei*, A Kumar, T Ma Neural Information Processing Systems (NeurIPS), 2020 | 85 | 2020 |
| Openai o1 system card A Jaech, A Kalai, A Lerer, A Richardson, A El-Kishky, A Low, A Helyar, ... arXiv preprint arXiv:2412.16720, 2024 | 73 | 2024 |
| 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 International Conference on Learning Representations (ICLR), 2021 | 65 | 2021 |
| Selective Classification Can Magnify Disparities Across Groups E Jones*, S Sagawa*, PW Koh*, A Kumar, P Liang International Conference on Learning Representations (ICLR), 2021 | 60 | 2021 |
| Consistent generative query networks A Kumar, SM Eslami, DJ Rezende, M Garnelo, F Viola, E Lockhart, ... NeurIPS workshop on Bayesian Deep Learning, 2018 | 50* | 2018 |
| Beyond separability: Analyzing the linear transferability of contrastive representations to related subpopulations JZ HaoChen, C Wei, A Kumar, T Ma Neural Information Processing Systems (NeurIPS), 2022 | 42 | 2022 |
| Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift A Kumar, T Ma, P Liang, A Raghunathan Conference on Uncertainty in Artificial Intelligence (UAI), 2022 | 41* | 2022 |
| No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets F Tajwar, A Kumar*, SM Xie*, P Liang ICML UDL Workshop, 2021 | 23 | 2021 |
Percy LiangAssociate Professor of Computer Science, Stanford UniversityEmail được xác minh tại cs.stanford.edu