Data Quality Evaluation & Efficient Data Generation
Imperfect Data Curation & Influence Estimation
Trustworthy and Efficient Learning under Imperfect Real-World Data
[22] Controlled HALlucination-Evaluation (CHALE) Benchmark
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Andrew Estornell, Zhaowei Zhu, Yang Liu
(Under Review) [data] [Category: LLM Dataset]
[21] Automatic dataset construction (ADC): Sample collection, data curation, and beyond
Minghao Liu*, Zonglin Di*, Jiaheng Wei*, etc
(Under Review) [paper] [Category: Benchmark, Open-Source software, LLM data collection]
[20] Measuring and Reducing LLM Hallucination without Gold-Standard Answers
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo, Andrew Estornell, Yang Liu
(Under Review) [paper] [Category: LLM Evaluation; LLM Alignment; In-Context Learning]
[19] 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]
[18] 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]
[17] Beyond Data Sharing: Incentivizing Heterogeneous Clients in Strategic Federated Learning
Jinlong Pang, Jiaheng Wei, Chen Qian, Yang Liu
(Under Review) [paper] [Category: Information Elicitation]
[16] Consensus on Dynamic Stochastic Block Models: Fast Convergence and Phase Transitions
Haoyu Wang, Jiaheng Wei, Zhenyuan Zhang
(Under Review) [paper] [Category: Majority Dynamics]
[15] Harnessing Business and Media Insights with Large Language Models
Center for Advanced AI, Accenture & Fortune Media
[tech report] [product]
[Category: LLM Training; Supervised Fine-Tuning]
[14] On the Generalization and Adaptation Ability of Machine-Generated Text Detectors in Academic Writing
Yule Liu, Zhiyuan Zhong, Yifan Liao, Zhen Sun, Jingyi Zheng, Jiaheng Wei, Qingyuan Gong, Fenghua Tong, Yang Chen, Yang Zhang, Xinlei He
KDD – ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025 (Dataset & Benchmark Track)
[paper] [Category: LLM Unlearning]
[13] Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-tuning
Jinlong Pang, Na Di, Zhaowei Zhu, Jiaheng Wei, Hao Cheng, Chen Qian, Yang Liu
ICML – International Conference on Machine Learning, 2025
[paper] [Category: LLM SFT]
[12] LLM Unlearning via Loss Adjustments with Only Forget Data
Yaxuan Wang, Jiaheng Wei#, Chris Yuhao Liu, Jinlong Pang, Quan Liu, Ankit Parag Shah, Yujia Bao, Yang Liu, Wei Wei
ICLR – International Conference on Learning Representations, 2025
[paper] [Category: LLM Unlearning]
[11] Improving Data Efficiency via Curating LLM-Driven Rating Systems
Jinlong Pang, Jiaheng Wei#, Ankit Parag Shah, Zhaowei Zhu, Yaxuan Wang, Chen Qian, Yang Liu, Yujia Bao, Wei Wei
ICLR – International Conference on Learning Representations, 2025
[paper] [Category: High quality data selection; LLM Machine Alignment]
[10] Do humans and machines have the same eyes? Human-machine perceptual differences on image classification.
Minghao Liu*, Jiaheng Wei*, Yang Liu, James Davis
AAAI (Oral) – The Association for the Advancement of Artificial Intelligence, 2025
[paper] [Category: Human-in-the-loop]
[9] Distributionally Robust Post-hoc Classifiers 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]
[8] 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]
[7] Auditing for Federated Learning: A Model Elicitation Approach
Yang Liu, Rixing Lou, Jiaheng Wei [Alphabetical order]
DAI – Distributed AI, 2023
[paper] [Category: Information Elicitation]
[6] 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]
[5] 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]
[4] 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]
[3] 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]
[2] 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]
[1] 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]