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Found 6 papers, 0 papers with code
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
While supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored.
Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
Unsupervised learning has been widely used in many real-world applications.
Spectral clustering via adaptive layer aggregation for multi-layer networks
One of the fundamental problems in network analysis is detecting community structure in multi-layer networks, of which each layer represents one type of edge information among the nodes.
Does SLOPE outperform bridge regression?
A recently proposed SLOPE estimator (arXiv:1407. 3824) has been shown to adaptively achieve the minimax $\ell_2$ estimation rate under high-dimensional sparse linear regression models (arXiv:1503. 08393).
Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms
Our contributions herein, enhance our prior work on nuclear norm regularized problems for matrix completion (Mazumder et al., 2010) by incorporating a continuum of nonconvex penalty functions between the convex nuclear norm and nonconvex rank functions.
On the estimation of correlation in a binary sequence model
We consider a binary sequence generated by thresholding a hidden continuous sequence.
