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Found 4 papers, 1 papers with code
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
This paper addresses this fundamental question by proving that, surprisingly, classical Maximum Likelihood Estimation (MLE) purely using source data (without any modification) achieves the minimax optimality for covariate shift under the well-specified setting.
On the Provable Advantage of Unsupervised Pretraining
Unsupervised pretraining, which learns a useful representation using a large amount of unlabeled data to facilitate the learning of downstream tasks, is a critical component of modern large-scale machine learning systems.
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond
We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared.
Second-order Information in First-order Optimization Methods
To the best of our knowledge, this is the first paper that seriously considers the necessity of square root among all adaptive methods.
