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2020 – today
- 2024
[i17]Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, Yang Liu:
Human-Instruction-Free LLM Self-Alignment with Limited Samples. CoRR abs/2401.06785 (2024)- [i16]Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo, Andrew Estornell, Yang Liu:
Measuring and Reducing LLM Hallucination without Gold-Standard Answers via Expertise-Weighting. CoRR abs/2402.10412 (2024) - [i15]Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang, Ming Ding, Chao Chen, Kok-Leong Ong, Jun Zhang, Yang Xiang:
Memorization in deep learning: A survey. CoRR abs/2406.03880 (2024) - [i14]Yujia Bao, Ankit Parag Shah, Neeru Narang, Jonathan Rivers, Rajeev Maksey, Lan Guan, Louise N. Barrere, Shelley Evenson, Rahul Basole, Connie Miao, Ankit Mehta, Fabien Boulay, Su Min Park, Natalie E. Pearson, Eldhose Joy, Tiger He, Sumiran Thakur, Koustav Ghosal, Josh On, Phoebe Morrison, Tim Major, Eva Siqi Wang, Gina Escobar, Jiaheng Wei, Tharindu Cyril Weerasooriya, Queena Song, Daria Lashkevich, Clare Chen, Gyuhak Kim, Dengpan Yin, Don Hejna, Mo Nomeli, Wei Wei:
Harnessing Business and Media Insights with Large Language Models. CoRR abs/2406.06559 (2024) - [i13]Minghao Liu, Zonglin Di, Jiaheng Wei, Zhongruo Wang, Hengxiang Zhang, Ruixuan Xiao, Haoyu Wang, Jinlong Pang, Hao Chen, Ankit Shah, Hongxin Wei, Xinlei He, Zhaowei Zhao, Haobo Wang, Lei Feng, Jindong Wang, James Davis, Yang Liu:
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond. CoRR abs/2408.11338 (2024) - 2023
- [c9]Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu:
To Aggregate or Not? Learning with Separate Noisy Labels. CSW@WSDM 2023: 8-43 - [c8]Yang Liu
, Rixing Lou, Jiaheng Wei:
Auditing for Federated Learning: A Model Elicitation Approach. DAI 2023: 12:1-12:9 - [c7]Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar:
Distributionally Robust Post-hoc Classifiers under Prior Shifts. ICLR 2023 - [c6]Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu:
To Aggregate or Not? Learning with Separate Noisy Labels. KDD 2023: 2523-2535 - [i12]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i11]Minghao Liu, Jiaheng Wei, Yang Liu, James Davis:
Do humans and machines have the same eyes? Human-machine perceptual differences on image classification. CoRR abs/2304.08733 (2023) - [i10]Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang, Chao Chen, Shirui Pan, Kok-Leong Ong, Jun Zhang, Yang Xiang:
Client-side Gradient Inversion Against Federated Learning from Poisoning. CoRR abs/2309.07415 (2023) - [i9]Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar:
Distributionally Robust Post-hoc Classifiers under Prior Shifts. CoRR abs/2309.08825 (2023) - 2022
- [c5]Jiaheng Wei, Minghao Liu, Jiahao Luo, Andrew Zhu, James Davis, Yang Liu:
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training. ECCV (23) 2022: 290-317 - [c4]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. ICLR 2022 - [c3]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [i8]Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu:
To Aggregate or Not? Learning with Separate Noisy Labels. CoRR abs/2206.07181 (2022) - [i7]Haoyu Wang, Jiaheng Wei, Zhenyuan Zhang:
Consensus on Dynamic Stochastic Block Models: Fast Convergence and Phase Transitions. CoRR abs/2209.03999 (2022) - 2021
- [c2]Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang:
Sample Elicitation. AISTATS 2021: 2692-2700 - [c1]Jiaheng Wei, Yang Liu:
When Optimizing f-Divergence is Robust with Label Noise. ICLR 2021 - [i6]Jiaheng Wei, Minghao Liu, Jiahao Luo, Qiutong Li, James Davis, Yang Liu:
PeerGAN: Generative Adversarial Networks with a Competing Peer Discriminator. CoRR abs/2101.07524 (2021) - [i5]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu:
Understanding (Generalized) Label Smoothing when Learning with Noisy Labels. CoRR abs/2106.04149 (2021) - [i4]Yang Liu, Yatong Chen, Jiaheng Wei:
Induced Domain Adaptation. CoRR abs/2107.05911 (2021) - [i3]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. CoRR abs/2110.12088 (2021) - 2020
- [i2]Yang Liu, Jiaheng Wei:
Incentives for Federated Learning: a Hypothesis Elicitation Approach. CoRR abs/2007.10596 (2020) - [i1]Jiaheng Wei, Yang Liu:
When Optimizing f-divergence is Robust with Label Noise. CoRR abs/2011.03687 (2020)
Coauthor Index
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