research
∙
07/05/2023
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Federated learning is a decentralized and privacy-preserving technique t...
share
research
∙
07/05/2023
Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix
To improve the robustness of graph neural networks (GNN), graph structur...
research
∙
05/20/2023
Stability and Generalization of â„“_p-Regularized Stochastic Learning for GCN
Graph convolutional networks (GCN) are viewed as one of the most popular...
research
∙
11/16/2021
SStaGCN: Simplified stacking based graph convolutional networks
Graph convolutional network (GCN) is a powerful model studied broadly in...
research
∙
10/18/2021
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
This paper considers the partially functional linear model (PFLM) where ...
research
∙
02/28/2021
Communication-efficient Byzantine-robust distributed learning with statistical guarantee
Communication efficiency and robustness are two major issues in modern d...
research
∙
02/20/2021
Generalization bounds for graph convolutional neural networks via Rademacher complexity
This paper aims at studying the sample complexity of graph convolutional...
research
∙
06/05/2020
Learning rates for partially linear support vector machine in high dimensions
This paper analyzes a new regularized learning scheme for high dimension...
research
∙
12/02/2019
Financial Market Directional Forecasting With Stacked Denoising Autoencoder
Forecasting stock market direction is always an amazing but challenging ...
research
∙
02/26/2018
Scalable kernel-based variable selection with sparsistency
Variable selection is central to high-dimensional data analysis, and var...
research
∙
08/18/2017
