Learning Distributions from Multiple Data Providers
Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples. The goal is to learn an unknown distribution $p$ on a finite domain $[n]$. The learner is given a fixed family of queryable sets $\mathscr{S} \subseteq 2^{[n]}$, and each query to $S \in \mathscr{S}$ returns an independent sample from the conditional distribution $p(\cdot \mid S)$. Learnability is governed by the co-occurrence graph associated with $\mathscr{S}$: two domain elements are adjacent if they appear together in some queryable set. Pointwise consistency is achievable when this graph is connected on the target support. PA...
| Authors | Jon Kleinberg, Amin Saberi, Xizhi Tan et al. |
|---|---|
| Primary category | cs.DS |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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