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Yunfeng Shao 0001

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2 papers

1 author row

UAI Conference 2023 Conference Paper

A constrained Bayesian approach to out-of-distribution prediction

Ziyu Wang 0006
Binjie Yuan
Jiaxun Lu
Bowen Ding
Yunfeng Shao 0001
Qibin Wu
Jun Zhu 0001

Consider the problem of out-of-distribution prediction given data from multiple environments. While a sufficiently diverse collection of training environments will facilitate the identification of an invariant predictor, with an optimal generalization performance, many applications only provide us with a limited number of environments. It is thus necessary to consider adapting to distribution shift using a handful of labeled test samples. We propose a constrained Bayesian approach for this task, which restricts to models with a worst-group training loss above a prespecified threshold. Our method avoids a pathology of the standard Bayesian posterior, which occurs when spurious correlations improve in-distribution prediction. We also show that on certain high-dimensional linear problems, constrained modeling improves the sample efficiency of adaptation. Synthetic and real-world experiments demonstrate the robust performance of our approach.

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ICML Conference 2022 Conference Paper

Personalized Federated Learning via Variational Bayesian Inference

Xu Zhang 0011
Yinchuan Li
Wenpeng Li
Kaiyang Guo
Yunfeng Shao 0001

Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a novel personalized federated learning method via Bayesian variational inference named pFedBayes. To alleviate the overfitting, weight uncertainty is introduced to neural networks for clients and the server. To achieve personalization, each client updates its local distribution parameters by balancing its construction error over private data and its KL divergence with global distribution from the server. Theoretical analysis gives an upper bound of averaged generalization error and illustrates that the convergence rate of the generalization error is minimax optimal up to a logarithmic factor. Experiments show that the proposed method outperforms other advanced personalized methods on personalized models, e. g. , pFedBayes respectively outperforms other SOTA algorithms by 1. 25%, 0. 42% and 11. 71% on MNIST, FMNIST and CIFAR-10 under non-i. i. d. limited data.

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