统一视角解析无归一化分布学习中的噪声对比估计方法
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
- 以噪声对比估计为统一框架，整合多类分布学习方法
- 在正则条件下，给出指数族模型的有限样本收敛速率
- 为不同研究领域的方法提供新理解，适合理论研究者
本文研究基于噪声对比估计（NCE）的一类用于学习无归一化分布的估计器。主要贡献是通过NCE的视角，为若干独立提出并分别研究于不同研究领域的无归一化分布学习方法提供了统一的理解。这一统一视角为已有估计器带来了新的洞见。具体而言，针对指数族，在一组正则性假设下，建立了所提估计器的有限样本收敛速率，其中大多数假设为新提出。
原文摘要 · Abstract (English)
This paper studies a family of estimators based on noise-contrastive estimation (NCE) for learning unnormalized distributions. The main contribution of this work is to provide a unified perspective on various methods for learning unnormalized distributions, which have been independently proposed and studied in separate research communities, through the lens of NCE. This unified view offers new insights into existing estimators. Specifically, for exponential families, we establish the finite-sample convergence rates of the proposed estimators under a set of regularity assumptions, most of which are new.
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