2018/02/11 by Marina Munkhoeva, Yermek Kapushev, Munkhoeva, Marina +5 · 16 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Applied mathematics #Artificial intelligence #Computer science #Convergence (economics) #Discrete mathematics #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Kernel (algebra) #Kernel embedding of distributions #Kernel method #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical optimization #Mathematics #Model Reduction and Neural Networks #Monte Carlo method #Statistics #Support vector machine #Variable kernel density estimation #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1802.03832
published in arXiv (Cornell University) (Cornell University) · Accepted to NIPS 2018; 9 pages, 3 figures, Appendix: 4 pages, 2 figures
openalex publication_date 2018/02/11 · arxiv created 2018/10/29 · arxiv updated 2018/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a unifying approach that reinterprets the previous random features methods and extends to better estimates of the kernel approximation. We derive the convergence behaviour and conduct an extensive empirical study that supports our hypothesis.