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Jiaheng Wei
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Year
Learning with noisy labels revisited: A study using real-world human annotations
J Wei, Z Zhu, H Cheng, T Liu, G Niu, Y Liu
ICLR 2022, 2022
1932022
To smooth or not? when label smoothing meets noisy labels
J Wei, H Liu, T Liu, G Niu, M Sugiyama, Y Liu
ICML 2022 (Long Presentation), 2022
66*2022
When optimizing -divergence is robust with label noise
J Wei, Y Liu
ICLR 2021, 2021
502021
To aggregate or not? learning with separate noisy labels
J Wei, Z Zhu, T Luo, E Amid, A Kumar, Y Liu
KDD 2023, 2023
202023
Incentives for federated learning: a hypothesis elicitation approach
Y Liu, J Wei
ICML 2020 (workshop), 2020
192020
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
J Wei, M Liu, J Luo, A Zhu, J Davis, Y Liu
ECCV 2022, 2022
15*2022
Distributionally Robust Post-hoc Classifiers under Prior Shifts
J Wei, H Narasimhan, E Amid, WS Chu, Y Liu, A Kumar
ICLR 2023, 2023
112023
Fairness improves learning from noisily labeled long-tailed data
J Wei, Z Zhu, G Niu, T Liu, S Liu, M Sugiyama, Y Liu
arXiv preprint arXiv:2303.12291, 2023
62023
Do humans and machines have the same eyes? human-machine perceptual differences on image classification
M Liu, J Wei, Y Liu, J Davis
arXiv preprint arXiv:2304.08733, 2023
52023
Human-instruction-free llm self-alignment with limited samples
H Guo, Y Yao, W Shen, J Wei, X Zhang, Z Wang, Y Liu
arXiv preprint arXiv:2401.06785, 2024
42024
Sample Elicitation
J Wei, Z Fu, Y Liu, X Li, Z Yang, Z Wang
AISTATS 2021, 2692-2700, 2021
32021
Measuring and Reducing LLM Hallucination without Gold-Standard Answers via Expertise-Weighting
J Wei, Y Yao, JF Ton, H Guo, A Estornell, Y Liu
arXiv preprint arXiv:2402.10412, 2024
12024
Auditing for federated learning: A model elicitation approach
Y Liu, R Lou, J Wei
Proceedings of the Fifth International Conference on Distributed Artificial …, 2023
12023
Consensus on Dynamic Stochastic Block Models: Fast Convergence and Phase Transitions
H Wang, J Wei, Z Zhang
arXiv preprint arXiv:2209.03999, 2022
12022
Incentivizing Data Collection from Heterogeneous Clients in Federated Learning
J Pang, J Wei, C Qian, Y Liu
2023
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