Publications and Preprints
Google Scholar & Semantic Scholoar
Large Language Models
Harnessing Business and Media Insights with Large Language Models
Center for Advanced AI, Accenture & Fortune Media
[paper] [product]
[Category: LLM Training; Supervised Fine-Tuning]Measuring and Reducing LLM Hallucination without Gold-Standard Answers
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo, Andrew Estornell, Yang Liu
(Under Review) [Category: LLM Evaluation; LLM Alignment; In-Context Learning; Supervised Fine-Tuning]Human-Instruction-Free LLM Self-Alignment with Limited Samples
Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, Yang Liu
(Under Review) [paper] [Category: LLM Alignment; In-Context Learning]
Trustworthy Machine Learning
To Aggregate or Not? Learning with Separate Noisy Labels
Jiaheng Wei*, Zhaowei Zhu*, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu
KDD – ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
[paper] [Category: Label Noise]Distributionally Robust Post-hoc Clasifiers under Prior Shifts
Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wensheng Chu, Yang Liu, Abhishek Kumar
ICLR – International Conference on Learning Representations, 2023
[paper] [code] [Category: Long-Tailed Learning, Group-Dro]To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu
ICML (Long Presentation) – International Conference on Machine Learning, 2022
[paper] [code] [Category: Label Noise]Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
Jiaheng Wei*, Zhaowei Zhu*, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu
ICLR – International Conference on Learning Representations, 2022
[paper] [data] [code] [Category: Label Noise]When Optimizing f-divergence is Robust with Label Noise
Jiaheng Wei and Yang Liu
ICLR – International Conference on Learning Representations, 2021
[paper] [code] [Category: Label Noise]Fairness Improve Learning from Noisily Labeled Long-Tailed Data
Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
(Under Review) [paper] [Category: Long-Tailed Learning]Do humans and machines have the same eyes? Human-machine perceptual differences on image classification.
Minghao Liu, Jiaheng Wei, Yang Liu, James Davis
(Under Review) [paper] [Category: Human-in-the-loop]
Incentives in Machine Learning
Sample Elicitation
Jiaheng Wei*, Zuyue Fu*, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang
AISTATS – International Conference on Artificial Intelligence and Statistics, 2021
[paper] [code] [Category: Information Elicitation]Auditing for Federated Learning: A Model Elicitation Approach
Yang Liu, Rixing Lou, Jiaheng Wei [Alphabetical order] DAI – Distributed AI, 2023
[paper] [Category: Information Elicitation]Incentives for Federated Learning: a Hypothesis Elicitation Approach
Yang Liu, Jiaheng Wei
ICML workshop – Workshop on Incentives in Machine Learning, 2020
[paper] [Category: Information Elicitation]Beyond Data Sharing: Incentivizing Heterogeneous Clients in Strategic Federated Learning
Jinlong Pang, Jiaheng Wei, Chen Qian, Yang Liu
(Under Review) [Category: Information Elicitation]
Other Papers
DuelGAN: A Duel between Two Discriminators Stabilizes the GAN Training
Jiaheng Wei*, Minghao Liu*, Jiahao Luo, Andrew Zhu, James Davis, Yang Liu
ECCV – European Conference on Computer Vision, 2022
[paper] [code] [Category: Deep Generative Models]Consensus on Dynamic Stochastic Block Models: Fast Convergence and Phase Transitions
Haoyu Wang, Jiaheng Wei, Zhenyuan Zhang
(Under Review) [paper] [Category: Majority Dynamics]
(*: denotes equal contributions)