2020/09/14 by Nicholas Smith, Smith, Nicholas
Computer Science · Mathematics · #Algorithm #Applied mathematics #Computer science #Estimator #FOS: Computer and information sciences #Function (biology) #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Geometry #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematics #Neural Networks and Applications #Scaling #Statistics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2009.06172
arxiv created 2020/09/14 · openalex publication_date 2020/09/14 · arxiv updated 2020/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
An ensemble method is introduced that utilizes randomization and loss function gradients to compute a prediction. Multiple weakly-correlated estimators approximate the gradient at randomly sampled points on the error surface and are aggregated into a final solution. A scaling parameter is described that controls a trade-off between ensemble correlation and precision. Numerical methods for estimating optimal values of the parameter are described. Empirical results are computed over a popular dataset. Inferential statistics on these results show that the method is capable of outperforming existing techniques in terms of increased accuracy.