Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
International Conference on Artificial Intelligence and Statistics , pages=
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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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Online Survival Analysis: A Bandit Approach under Cox PH Model
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
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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.