Scalable and Adaptive Distribution Regression via Random Features
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 distribution regression remains computationally expensive and sensitive to kernel choice, motivating scalable and adaptive representations. Building on random feature approximations, we develop a finite-sample analysis of Random Feature Distribution Regression (RaDR) under two-stage sampling. Our perturbation analysis separates the effects of distribution-level sampling, finite within-bag sampling, and random feature approximation. We establish suf...
| Authors | Yinsong Wang, Xiao Liu, Yu Ding |
|---|---|
| Primary category | stat.ML |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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