Scalable and Adaptive Distribution Regression via Random Features
arXiv:2610.24892v1 Announce Type: new Abstract: Distribution regression seeks to learn mappings from probability distributions to response variables and has emerged as a unifying framework for kernel methods, multi-instance learning, and set-valued learning. Despite its success, kernel-based distri…
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- 2026-10-20 04:00 · arXiv stat.ML
Scalable and Adaptive Distribution Regression via Random Features