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Found 4 papers, 1 papers with code
Stochastic Primal Dual Coordinate Method with Non-Uniform Sampling Based on Optimality Violations
We study primal-dual type stochastic optimization algorithms with non-uniform sampling.
Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier.
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
A significant advantage of considering them simultaneously rather than individually is that they have a synergy effect in the sense that the results of the previous safe feature screening can be exploited for improving the next safe sample screening performances, and vice-versa.
Regularization Path of Cross-Validation Error Lower Bounds
Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances.
