IEEE

Leveraging Simplex Gradient Variance and Bias Reduction for Black-Box Optimization of Noisy and Costly Functions

Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true optimum. We ...
IEEE

Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine

Abstract: Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of ...