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Found 4 papers, 0 papers with code
Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem
This paper proposes an asynchronous parallel thresholding algorithm and its parameter-free version to improve the efficiency and the applicability.
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity
In this paper, we proposed a general bilevel exclusive sparsity formulation to pursue the diversity by restricting the overall sparsity and the sparsity in each group.
Exclusive Sparsity Norm Minimization with Random Groups via Cone Projection
To the best of our knowledge, this is the first time to guarantee such convergence rate for the general exclusive sparsity norm minimization; 2) When the group information is unavailable to define the exclusive sparsity norm, we propose to use the random grouping scheme to construct groups and prove that if the number of groups is appropriately chosen, the nonzeros (true features) would be grouped in the ideal way with high probability.
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently.
