3 citations · 11 across the 19 of their papers we have counts for
5 papers · 1 filter
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
Junliang Lyu, Yixuan Zhang, Xiaoling Lu +1
This work addresses a key limitation in current federated learning approaches, which predominantly focus on homogeneous tasks, neglecting the task diversity on local devices. We pr…
Navigating Towards Fairness with Data Selection
Yixuan Zhang, Zhidong Li, Yang Wang +3
Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fai…
Nonstationary Sparse Spectral Permanental Process
Zicheng Sun, Yixuan Zhang, Zenan Ling +2
Existing permanental processes often impose constraints on kernel types or stationarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel appr…
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
Yixuan Zhang, Feng Zhou
Fine-tuning pre-trained models is a widely employed technique in numerous real-world applications. However, fine-tuning these models on new tasks can lead to unfair outcomes. This…
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Zenan Ling, Longbo Li, Zhanbo Feng +4
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding…