no code implementations • 16 Feb 2024 • Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo, Andrew Estornell, Yang Liu
In this work, we propose Factualness Evaluations via Weighting LLMs (FEWL), the first hallucination metric that is specifically designed for the scenario when gold-standard answers are absent.
no code implementations • 6 Jan 2024 • Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, Yang Liu
The key idea is to first retrieve high-quality samples related to the target domain and use them as In-context Learning examples to generate more samples.
1 code implementation • 16 Sep 2023 • Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar
We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors.
no code implementations • 14 Sep 2023 • Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang, Chao Chen, Shirui Pan, Kok-Leong Ong, Jun Zhang, Yang Xiang
For the first time, we show the feasibility of a client-side adversary with limited knowledge being able to recover the training samples from the aggregated global model.
no code implementations • 18 Apr 2023 • Minghao Liu, Jiaheng Wei, Yang Liu, James Davis
Trained computer vision models are assumed to solve vision tasks by imitating human behavior learned from training labels.
no code implementations • 22 Mar 2023 • Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning.
no code implementations • 14 Jun 2022 • Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e. g., via crowdsourcing).
2 code implementations • ICLR 2022 • Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu
These observations require us to rethink the treatment of noisy labels, and we hope the availability of these two datasets would facilitate the development and evaluation of future learning with noisy label solutions.
no code implementations • 29 Sep 2021 • Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu
It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model.
Ranked #15 on Learning with noisy labels on CIFAR-10N-Worst
1 code implementation • 8 Jun 2021 • Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu
We provide understandings for the properties of LS and NLS when learning with noisy labels.
Ranked #9 on Learning with noisy labels on CIFAR-10N-Random3
no code implementations • 19 Jan 2021 • Jiaheng Wei, Minghao Liu, Jiahao Luo, Andrew Zhu, James Davis, Yang Liu
In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse.
Ranked #3 on Image Generation on Fashion-MNIST
2 code implementations • ICLR 2021 • Jiaheng Wei, Yang Liu
We show when maximizing a properly defined $f$-divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise.
Ranked #13 on Learning with noisy labels on CIFAR-10N-Aggregate
no code implementations • 21 Jul 2020 • Yang Liu, Jiaheng Wei
The success of a credible federated learning system builds on the assumption that the decentralized and self-interested users will be willing to participate to contribute their local models in a trustworthy way.
1 code implementation • 8 Oct 2019 • Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang
We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.