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07/05/2023
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Federated learning is a decentralized and privacy-preserving technique t...
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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...
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05/20/2023
Stability and Generalization of ℓ_p-Regularized Stochastic Learning for GCN
Graph convolutional networks (GCN) are viewed as one of the most popular...
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11/16/2021
SStaGCN: Simplified stacking based graph convolutional networks
Graph convolutional network (GCN) is a powerful model studied broadly in...
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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 ...
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02/28/2021
Communication-efficient Byzantine-robust distributed learning with statistical guarantee
Communication efficiency and robustness are two major issues in modern d...
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02/20/2021
Generalization bounds for graph convolutional neural networks via Rademacher complexity
This paper aims at studying the sample complexity of graph convolutional...
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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...
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12/02/2019
Financial Market Directional Forecasting With Stacked Denoising Autoencoder
Forecasting stock market direction is always an amazing but challenging ...
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02/26/2018
Scalable kernel-based variable selection with sparsistency
Variable selection is central to high-dimensional data analysis, and var...
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08/18/2017
