Summary of A Unified View on Learning Unnormalized Distributions Via Noise-contrastive Estimation, by J. Jon Ryu et al.
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
by J. Jon Ryu, Abhin Shah, Gregory W. Wornell
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A family of estimators based on noise-contrastive estimation (NCE) is proposed for learning unnormalized distributions. This paper provides a unified perspective on various methods for learning unnormalized distributions, offering new insights into existing estimators. The main contribution establishes finite-sample convergence rates of the proposed estimators under regularity assumptions, most of which are new. The proposed method is applied to exponential families and evaluated using various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to learn about things we can’t measure directly. It brings together different ideas from separate research communities into a single framework called NCE. This makes it easier to compare and understand these methods. The researchers also show that their approach works well in certain situations, like with exponential families. |
