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This work proposes a time-smoothed proximal linear algorithm and a local-regret measure based on a proximal residual mapping that is a proper stationarity measure for the original problem: its fixed-point condition implies first-order stationarity.
This work proposes a statistically consistent and scalable estimator for score differences based on Sobolev regularization and demonstrates its effectiveness on real-world tasks, including transfer learning for ECG signal generation, where it substantially outperforms non-regularized score difference estimators in down...
A decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization and establishes a non-asymptotic upper bound on the excess decision risk of W2S and a complementary lower bound for a strong-only benchmark.
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