IEEE

A Family of Hyperparameter Estimators Linking EB and SURE for Kernel-Based Regularization Methods

Abstract: Hyperparameter estimation is a critical aspect of kernel-based regularization methods (KRMs), alongside kernel design. Empirical Bayes (EB) and Stein's unbiased risk estimator (SURE) are two ...
IEEE

Congruent Learning for Self-Regulated Federated Learning in 6G

Abstract: Future 6G networks are expected to be AI-native with distributed machine learning functionalities responsible for improving and automating a variety of network- and service-management tasks.
GitHub

Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition (NeurIPS 2023)

This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node ...
C&EN

Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites

Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan Research Center for Artificial Intelligence in Medicine, Taipei ...
Europe PMC

VdistCox: Vertically distributed Cox proportional hazards model with hyperparameter optimization.

Vertically partitioned data is distributed data in which information about a patient is distributed across multiple sites. In this study, we propose a novel algorithm (referred to as VdistCox) for the ...
Microsoft

µTransfer: A technique for hyperparameter tuning of enormous neural networks

If our hypothesis is correct, the training loss-hyperparameter curves for µP models of different widths would share a similar minimum. Conversely, our reasoning suggests that no scaling rule of ...