Obserwuj
Yang Liu
Tytuł
Cytowane przez
Cytowane przez
Rok
Actionable recourse in linear classification
B Ustun, A Spangher, Y Liu
ACM Conference on Fairness, Accountability, and Transparency, 2019
6042019
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
386*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
298*2019
Peer loss functions: Learning from noisy labels without knowing noise rates
Y Liu, H Guo
International conference on machine learning, 6226-6236, 2020
2402020
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
2392021
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
2142020
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
1822023
Fairness without harm: Decoupled classifiers with preference guarantees
B Ustun, Y Liu, D Parkes
International Conference on Machine Learning, 6373-6382, 2019
1482019
Reinforcement learning with perturbed rewards
J Wang, Y Liu, B Li
Proceedings of the AAAI conference on artificial intelligence 34 (04), 6202-6209, 2020
1442020
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
1372021
Calibrated fairness in bandits
Y Liu, G Radanovic, C Dimitrakakis, D Mandal, DC Parkes
arXiv preprint arXiv:1707.01875, 2017
1302017
Fair Classification with Group-Dependent Label Noise
J Wang, Y Liu*, C Levy
ACM Conference on Fairness, Accountability, and Transparency, 2021
1092021
Grinding the Space: Learning to Classify Against Strategic Agents
Y Chen, Y Liu, C Podimata
Advances in Neural Information Processing Systems (NeurIPS), 2020
100*2020
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
952020
An online learning approach to improving the quality of crowdsourcing
Y Liu, M Liu
ACM SIGMETRICS, 2015
932015
Federated bandit: A gossiping approach
Z Zhu, J Zhu, J Liu, Y Liu
Proceedings of the 2021 ACM SIGMETRICS/International Conference on …, 2021
912021
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
902021
Large language model unlearning
Y Yao, X Xu, Y Liu
arXiv preprint arXiv:2310.10683, 2023
852023
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
842021
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
82*2022
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