Summary of Fearless Stochasticity in Expectation Propagation, by Jonathan So et al.
Fearless Stochasticity in Expectation Propagation
by Jonathan So, Richard E. Turner
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper presents two new variants of expectation propagation (EP), a family of algorithms for approximate inference in probabilistic models. EP updates involve evaluating moments, which can be estimated from Monte Carlo samples. However, these updates are not robust to Monte Carlo noise when performed naively. The authors provide a novel perspective on the moment-matching updates of EP, viewing them as natural-gradient-based optimization of a variational objective. This insight motivates two new EP variants with updates well-suited for MC estimation. These new variants combine benefits of previous approaches and address key weaknesses, such as improved speed-accuracy trade-offs and no reliance on debiasing estimators. The paper demonstrates efficacy on various probabilistic inference tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a way to make computers better at understanding uncertainty in complex systems. The method uses something called “expectation propagation” (EP) which helps computers learn from uncertain data. EP can be noisy and unreliable, but the authors found a new way to improve it by looking at it like an optimization problem. This leads to two new versions of EP that are better than before and work well even with limited data. The paper shows how these new methods perform well on different tasks. |
Keywords
» Artificial intelligence » Inference » Optimization
