Výsledky vyhledávání - "Yin, Tongxin"

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Federated Learning with Reduced Information Leakage and Computation

Autor: Yin, Tongxin, Tan, Xuwei, Zhang, Xueru, Khalili, Mohammad Mahdi, Liu, Mingyan

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although local data is not exposed directly, privacy concerns nonetheless exist

Externí odkaz: http://arxiv.org/abs/2310.06341

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Fair Classifiers that Abstain without Harm

Autor: Yin, Tongxin, Ton, Jean-François, Guo, Ruocheng, Yao, Yuanshun, Liu, Mingyan, Liu, Yang

In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to m

Externí odkaz: http://arxiv.org/abs/2310.06205

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Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts

Autor: Jin, Kun, Yin, Tongxin, Chen, Zhongzhu, Sun, Zeyu, Zhang, Xueru, Liu, Yang, Liu, Mingyan

We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distributed data. Unlike the conventional FL framework that assumes the client

Externí odkaz: http://arxiv.org/abs/2305.05090

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Long-Term Fairness with Unknown Dynamics

Autor: Yin, Tongxin, Raab, Reilly, Liu, Mingyan, Liu, Yang

Publikováno v: Advances in Neural Information Processing Systems 36 (NeurIPS 2023)

While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness in the context of online reinforcement learning. This formulation can acco

Externí odkaz: http://arxiv.org/abs/2304.09362

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