A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.
arXiv preprint arXiv:1704.09011 , year=
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Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.
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Budgeted Online Influence Maximization
A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.
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Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.