Actionable recourse in linear classification B Ustun, A Spangher, Y Liu ACM Conference on Fairness, Accountability, and Transparency, 2019 | 583 | 2019 |
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 | 380* | 2015 |
How do fairness definitions fare? Testing public attitudes towards three algorithmic definitions of fairness in loan allocations NA Saxena, K Huang, E DeFilippis, G Radanovic, DC Parkes, Y Liu AAAI Conference on AI, Ethics, and Society, 2019 | 271* | 2019 |
Peer loss functions: Learning from noisy labels without knowing noise rates Y Liu, H Guo International conference on machine learning, 6226-6236, 2020 | 221 | 2020 |
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 | 205 | 2021 |
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 | 198 | 2020 |
Fairness without harm: Decoupled classifiers with preference guarantees B Ustun, Y Liu, D Parkes International Conference on Machine Learning, 6373-6382, 2019 | 136 | 2019 |
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 | 130 | 2021 |
Reinforcement learning with perturbed rewards J Wang, Y Liu, B Li Proceedings of the AAAI conference on artificial intelligence 34 (04), 6202-6209, 2020 | 129 | 2020 |
Calibrated fairness in bandits Y Liu, G Radanovic, C Dimitrakakis, D Mandal, DC Parkes arXiv preprint arXiv:1707.01875, 2017 | 123 | 2017 |
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment Y Liu, Y Yao, JF Ton, X Zhang, RGH Cheng, Y Klochkov, MF Taufiq, H Li arXiv preprint arXiv:2308.05374, 2023 | 119 | 2023 |
Fair Classification with Group-Dependent Label Noise J Wang, Y Liu*, C Levy ACM Conference on Fairness, Accountability, and Transparency, 2021 | 103 | 2021 |
An online learning approach to improving the quality of crowdsourcing Y Liu, M Liu ACM SIGMETRICS, 2015 | 91 | 2015 |
How do fair decisions fare in long-term qualification? X Zhang, R Tu, Y Liu, M Liu, H Kjellstrom, K Zhang, C Zhang Advances in Neural Information Processing Systems 33, 18457-18469, 2020 | 89 | 2020 |
Grinding the Space: Learning to Classify Against Strategic Agents Y Chen, Y Liu, C Podimata Advances in Neural Information Processing Systems (NeurIPS), 2020 | 89* | 2020 |
Federated bandit: A gossiping approach Z Zhu, J Zhu, J Liu, Y Liu Proceedings of the 2021 ACM SIGMETRICS/International Conference on …, 2021 | 86 | 2021 |
Clusterability as an alternative to anchor points when learning with noisy labels Z Zhu, Y Song, Y Liu International Conference on Machine Learning, 12912-12923, 2021 | 83 | 2021 |
Are gender-neutral queries really gender-neutral? mitigating gender bias in image search J Wang, Y Liu, XE Wang arXiv preprint arXiv:2109.05433, 2021 | 71 | 2021 |
Surrogate scoring rules Y Liu, J Wang, Y Chen ACM Transactions on Economics and Computation 10 (3), 1-36, 2023 | 69 | 2023 |
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 | 69* | 2022 |