Neural-Kernel Conditional Mean Embeddings

arXiv:2403.10859v1 Announce Type: cross
Abstract: Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing conditional distribution, but they often face scalability and expressiveness challenges. In this work, we propose a new method that effectively combines the strengths of deep learning with CMEs in order to address these challenges. Specifically, our approach leverages the end-to-end neural network (NN) optimization framework using a kernel-based objective. This design circumvents the computationally expensive Gram matrix inversion required by current CME methods. To …

abstract arxiv challenges cs.lg deep learning distribution embeddings face framework kernel mean scalability stat.ml the end type work