Summary of You Only Accept Samples Once: Fast, Self-correcting Stochastic Variational Inference, by Dominic B. Dayta
You Only Accept Samples Once: Fast, Self-Correcting Stochastic Variational Inference
by Dominic B. Dayta
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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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 We present YOASOVI, a novel algorithm for stochastic optimization in Variational Inference (VI) on large Bayesian hierarchical models. YOASOVI leverages information about the objective function to replace regular Monte Carlo sampling with acceptance sampling, reducing computational resources needed for gradient estimation. The algorithm is developed in two versions: one based on naive intuition and another as a Metropolis-type scheme. Empirical results on simulations and benchmark datasets for multivariate Gaussian mixture models demonstrate that YOASOVI outperforms regularized Monte Carlo and Quasi-Monte Carlo VI algorithms, converging faster (in clock time) and within better optimal neighborhoods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We created a new way to do complex math problems on big models called Bayesian hierarchical models. This method is called YOASOVI and it’s really fast and good at finding the best answer. It does this by using some tricks to reduce the amount of math calculations needed. We tested YOASOVI on some sample problems and compared it to other methods. The results show that YOASOVI is faster and better than others, making it a useful tool for people working with these big models. |
Keywords
» Artificial intelligence » Inference » Objective function » Optimization
