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Found 11 papers, 0 papers with code
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data.
Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix
Overall, our research highlights the importance of integrating feature and graph information alignment in GSL, as inspired by our derived theoretical result, and showcases the superiority of our approach in handling noisy graph structures through comprehensive experiments on real-world datasets.
Stability and Generalization of lp-Regularized Stochastic Learning for GCN
For a single-layer GCN, we establish an explicit theoretical understanding of GCN with the $\ell_p$-regularized stochastic learning by analyzing the stability of our SGD proximal algorithm.
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation
In this paper, we provide theoretical results of estimation bounds and excess risk upper bounds for support vector machine (SVM) with sparse multi-kernel representation.
SStaGCN: Simplified stacking based graph convolutional networks
Graph convolutional network (GCN) is a powerful model studied broadly in various graph structural data learning tasks.
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
This paper considers the partially functional linear model (PFLM) where all predictive features consist of a functional covariate and a high dimensional scalar vector.
Communication-efficient Byzantine-robust distributed learning with statistical guarantee
To address the two issues simultaneously, this paper develops two communication-efficient and robust distributed learning algorithms for convex problems.
Generalization bounds for graph convolutional neural networks via Rademacher complexity
This paper aims at studying the sample complexity of graph convolutional networks (GCNs), by providing tight upper bounds of Rademacher complexity for GCN models with a single hidden layer.
Financial Market Directional Forecasting With Stacked Denoising Autoencoder
Forecasting stock market direction is always an amazing but challenging problem in finance.
Efficient kernel-based variable selection with sparsistency
Variable selection is central to high-dimensional data analysis, and various algorithms have been developed.
Debiased distributed learning for sparse partial linear models in high dimensions
Although various distributed machine learning schemes have been proposed recently for pure linear models and fully nonparametric models, little attention has been paid on distributed optimization for semi-paramemetric models with multiple-level structures (e. g. sparsity, linearity and nonlinearity).
