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Found 6 papers, 2 papers with code
Barzilai-Borwein Step Size for Stochastic Gradient Descent
One of the major issues in stochastic gradient descent (SGD) methods is how to choose an appropriate step size while running the algorithm.
Stochastic Primal-Dual Method for Empirical Risk Minimization with $\mathcal{O}(1)$ Per-Iteration Complexity
Regularized empirical risk minimization problem with linear predictor appears frequently in machine learning.
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity
Regularized empirical risk minimization problem with linear predictor appears frequently in machine learning.
Central Server Free Federated Learning over Single-sided Trust Social Networks
However, in many social network scenarios, centralized federated learning is not applicable (e. g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable).
Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization
Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e. g., sparsity) efficiently.
Fast and Secure Distributed Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks.
