| How does mixup help with robustness and generalization? L Zhang, Z Deng, K Kawaguchi, A Ghorbani, J Zou arXiv preprint arXiv:2010.04819, 2020 | 286 | 2020 |
| The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy TT Cai, Y Wang, L Zhang The Annals of Statistics 49 (5), 2825-2850, 2021 | 208 | 2021 |
| Improving out-of-distribution robustness via selective augmentation H Yao, Y Wang, S Li, L Zhang, W Liang, J Zou, C Finn International Conference on Machine Learning, 25407-25437, 2022 | 201 | 2022 |
| Analyzing and mitigating object hallucination in large vision-language models Y Zhou, C Cui, J Yoon, L Zhang, Z Deng, C Finn, M Bansal, H Yao arXiv preprint arXiv:2310.00754, 2023 | 180 | 2023 |
| Improving generalization in meta-learning via task augmentation H Yao, LK Huang, L Zhang, Y Wei, L Tian, J Zou, J Huang International conference on machine learning, 11887-11897, 2021 | 98 | 2021 |
| CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality TT Cai, J Ma, L Zhang | 90 | 2019 |
| Holistic analysis of hallucination in gpt-4v (ision): Bias and interference challenges C Cui, Y Zhou, X Yang, S Wu, L Zhang, J Zou, H Yao arXiv preprint arXiv:2311.03287, 2023 | 88 | 2023 |
| High dimensional linear discriminant analysis: optimality, adaptive algorithm and missing data T Tony Cai, L Zhang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2019 | 68 | 2019 |
| When and how mixup improves calibration L Zhang, Z Deng, K Kawaguchi, J Zou International Conference on Machine Learning, 26135-26160, 2022 | 67 | 2022 |
| Meta-learning with fewer tasks through task interpolation H Yao, L Zhang, C Finn arXiv preprint arXiv:2106.02695, 2021 | 65 | 2021 |
| C-mixup: Improving generalization in regression H Yao, Y Wang, L Zhang, JY Zou, C Finn Advances in neural information processing systems 35, 3361-3376, 2022 | 62 | 2022 |
| Discover and cure: Concept-aware mitigation of spurious correlation S Wu, M Yuksekgonul, L Zhang, J Zou International Conference on Machine Learning, 37765-37786, 2023 | 60 | 2023 |
| Adversarial training helps transfer learning via better representations Z Deng, L Zhang, K Vodrahalli, K Kawaguchi, JY Zou Advances in Neural Information Processing Systems 34, 25179-25191, 2021 | 58 | 2021 |
| Estimation and inference for high-dimensional generalized linear models with knowledge transfer S Li, L Zhang, TT Cai, H Li Journal of the American Statistical Association 119 (546), 1274-1285, 2024 | 51 | 2024 |
| Exactly scale-free scale-free networks L Zhang, M Small, K Judd Physica A: Statistical Mechanics and its Applications 433, 182-197, 2015 | 45 | 2015 |
| What should data science education do with large language models X Tu, J Zou, WJ Su, L Zhang arXiv preprint arXiv:2307.02792 3, 2023 | 40 | 2023 |
| The power of contrast for feature learning: A theoretical analysis W Ji, Z Deng, R Nakada, J Zou, L Zhang Journal of Machine Learning Research 24 (330), 1-78, 2023 | 38 | 2023 |
| Understanding multimodal contrastive learning and incorporating unpaired data R Nakada, HI Gulluk, Z Deng, W Ji, J Zou, L Zhang International Conference on Artificial Intelligence and Statistics, 4348-4380, 2023 | 32 | 2023 |
| High-dimensional Gaussian copula regression: Adaptive estimation and statistical inference TT Cai, L Zhang Statistica Sinica, 963-993, 2018 | 30 | 2018 |
| A convex optimization approach to high-dimensional sparse quadratic discriminant analysis TT Cai, L Zhang The Annals of Statistics 49 (3), 1537-1568, 2021 | 28 | 2021 |
James ZouStanford Universitystanford.edu의 이메일 확인됨