no code implementations • 26 Apr 2024 • Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie, Qi Cao, Fei Sun, Jinyang Gao, HuaWei Shen, Bolin Ding
Specifically, the order-preserving reward incentivizes the model to verbalize greater confidence for responses of higher quality to align the order of confidence and quality.
no code implementations • 23 Feb 2024 • Yue Cui, Liuyi Yao, Zitao Li, Yaliang Li, Bolin Ding, Xiaofang Zhou
We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives.
no code implementations • 22 Feb 2024 • Shen Li, Liuyi Yao, Jinyang Gao, Lan Zhang, Yaliang Li
To support various applications, business owners often seek the customized models that are obtained by fine-tuning a pre-trained LLM through the API provided by LLM owners or cloud servers.
1 code implementation • 21 Feb 2024 • Dawei Gao, Zitao Li, Xuchen Pan, Weirui Kuang, Zhijian Ma, Bingchen Qian, Fei Wei, WenHao Zhang, Yuexiang Xie, Daoyuan Chen, Liuyi Yao, Hongyi Peng, Zeyu Zhang, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li, Bolin Ding, Jingren Zhou
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
no code implementations • 18 Feb 2024 • Jiamu Bai, Daoyuan Chen, Bingchen Qian, Liuyi Yao, Yaliang Li
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs).
no code implementations • 8 Feb 2024 • Zhenqing Ling, Daoyuan Chen, Liuyi Yao, Yaliang Li, Ying Shen
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing.
no code implementations • 2 Feb 2024 • Yue Cui, Liuyi Yao, Yaliang Li, Ziqian Chen, Bolin Ding, Xiaofang Zhou
This FL market allows clients to gain monetary reward by selling their own models and improve local model performance through the purchase of others' models.
2 code implementations • 4 May 2023 • Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li
To overcome these challenges, we propose a novel approach named pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models.
1 code implementation • 3 Feb 2023 • Zeyu Qin, Liuyi Yao, Daoyuan Chen, Yaliang Li, Bolin Ding, Minhao Cheng
We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments.
1 code implementation • 7 Jun 2022 • Liuyi Yao, Dawei Gao, Zhen Wang, Yuexiang Xie, Weirui Kuang, Daoyuan Chen, Haohui Wang, Chenhe Dong, Bolin Ding, Yaliang Li
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.
1 code implementation • 12 Apr 2022 • Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou
The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications.
1 code implementation • 11 Apr 2022 • Yuexiang Xie, Zhen Wang, Dawei Gao, Daoyuan Chen, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, Jingren Zhou
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals.
no code implementations • 29 Sep 2021 • Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, Jing Gao
To tackle these challenges, we propose a novel casual graph based fair prediction framework, which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph.
1 code implementation • 5 Feb 2020 • Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, Aidong Zhang
Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.
1 code implementation • NeurIPS 2018 • Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.
no code implementations • 14 Oct 2018 • Yaliang Li, Liuyi Yao, Nan Du, Jing Gao, Qi Li, Chuishi Meng, Chenwei Zhang, Wei Fan
Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.