Search Results for author: Liuyi Yao

Found 16 papers, 8 papers with code

When to Trust LLMs: Aligning Confidence with Response Quality

no code implementations26 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.

Text Generation

A Bargaining-based Approach for Feature Trading in Vertical Federated Learning

no code implementations23 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.

Vertical Federated Learning

Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning

no code implementations22 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.

On the Convergence of Zeroth-Order Federated Tuning for Large Language Models

no code implementations8 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.

Federated Learning Privacy Preserving

An Auction-based Marketplace for Model Trading in Federated Learning

no code implementations2 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.

Federated Learning Marketing +1

Efficient Personalized Federated Learning via Sparse Model-Adaptation

2 code implementations4 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.

Personalized Federated Learning

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

1 code implementation3 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.

Backdoor Attack Personalized Federated Learning

A Benchmark for Federated Hetero-Task Learning

1 code implementation7 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.

Federated Learning Meta-Learning +2

FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning

1 code implementation12 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.

Federated Learning Graph Learning

FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

1 code implementation11 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.

Federated Learning Hyperparameter Optimization

Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning

no code implementations29 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.

Fairness Graph structure learning

A Survey on Causal Inference

1 code implementation5 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.

BIG-bench Machine Learning Causal Inference

Representation Learning for Treatment Effect Estimation from Observational Data

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.

Causal Inference Representation Learning +1

Finding Similar Medical Questions from Question Answering Websites

no code implementations14 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.

Question Answering Retrieval

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