| Actionable recourse in linear classification B Ustun, A Spangher, Y Liu ACM Conference on Fairness, Accountability, and Transparency, 2019 | 796 | 2019 |
| Trustworthy llms: a survey and guideline for evaluating large language models' alignment Y Liu, Y Yao, JF Ton, X Zhang, R Guo, H Cheng, Y Klochkov, MF Taufiq, ... arXiv preprint arXiv:2308.05374, 2023 | 573 | 2023 |
| Large language model unlearning Y Yao, X Xu, Y Liu Advances in Neural Information Processing Systems 37, 105425-105475, 2024 | 413 | 2024 |
| Learning with noisy labels revisited: A study using real-world human annotations J Wei, Z Zhu, H Cheng, T Liu, G Niu, Y Liu arXiv preprint arXiv:2110.12088, 2021 | 401 | 2021 |
| Rethinking machine unlearning for large language models S Liu, Y Yao, J Jia, S Casper, N Baracaldo, P Hase, Y Yao, CY Liu, X Xu, ... Nature Machine Intelligence, 1-14, 2025 | 329 | 2025 |
| Peer loss functions: Learning from noisy labels without knowing noise rates Y Liu, H Guo International conference on machine learning, 6226-6236, 2020 | 325 | 2020 |
| How do fairness definitions fare? Examining public attitudes towards algorithmic definitions of fairness NA Saxena, K Huang, E DeFilippis, G Radanovic, DC Parkes, Y Liu Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 99-106, 2019 | 298 | 2019 |
| Learning with instance-dependent label noise: A sample sieve approach H Cheng, Z Zhu, X Li, Y Gong, X Sun, Y Liu arXiv preprint arXiv:2010.02347, 2020 | 293 | 2020 |
| Cloudy with a chance of breach: Forecasting cyber security incidents Y Liu, A Sarabi, J Zhang, P Naghizadeh, M Karir, M Bailey, M Liu 24th USENIX Security Symposium (USENIX Security 15), 1009-1024, 2015 | 283 | 2015 |
| Skywork reward model series CY Liu, L Zeng, L Jiacai, R Yan, J He, C Wang, S Yan, Y Liu, Y Zhou arXiv preprint arXiv:2410.18451, 2024 | 226* | 2024 |
| Model sparsity can simplify machine unlearning J Jia, J Liu, P Ram, Y Yao, G Liu, Y Liu, P Sharma, S Liu Advances in Neural Information Processing Systems 36, 51584-51605, 2023 | 209 | 2023 |
| Reinforcement learning with perturbed rewards J Wang, Y Liu, B Li Proceedings of the AAAI conference on artificial intelligence 34 (04), 6202-6209, 2020 | 194 | 2020 |
| A second-order approach to learning with instance-dependent label noise Z Zhu, T Liu, Y Liu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 177 | 2021 |
| Fairness without harm: Decoupled classifiers with preference guarantees B Ustun, Y Liu, D Parkes International Conference on Machine Learning, 6373-6382, 2019 | 173 | 2019 |
| Calibrated fairness in bandits Y Liu, G Radanovic, C Dimitrakakis, D Mandal, DC Parkes arXiv preprint arXiv:1707.01875, 2017 | 156 | 2017 |
| Rating network security posture and comparing network maliciousness M Liu, M Bailey, M Karir, Y Liu, J Zhang US Patent 10,038,703, 2018 | 146 | 2018 |
| Fair Classification with Group-Dependent Label Noise J Wang, Y Liu*, C Levy ACM Conference on Fairness, Accountability, and Transparency, 2021 | 142 | 2021 |
| To smooth or not? when label smoothing meets noisy labels J Wei, H Liu, T Liu, G Niu, M Sugiyama, Y Liu International Conference on Machine Learning, 2022 | 139* | 2022 |
| Grinding the Space: Learning to Classify Against Strategic Agents Y Chen, Y Liu, C Podimata Advances in Neural Information Processing Systems (NeurIPS), 2020 | 136* | 2020 |
| Are gender-neutral queries really gender-neutral? mitigating gender bias in image search J Wang, Y Liu, X Wang Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 135 | 2021 |
David C. ParkesGeorge F. Colony Professor of Computer Science, John A. Paulson Dean, Harvard UniversityVerified email at eecs.harvard.edu
