Summary of Understanding Stochastic Natural Gradient Variational Inference, by Kaiwen Wu and Jacob R. Gardner
Understanding Stochastic Natural Gradient Variational Inference
by Kaiwen Wu, Jacob R. Gardner
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Stochastic natural gradient variational inference (NGVI) is a widely used posterior inference method in probabilistic models. Despite its popularity, the non-asymptotic convergence rate of stochastic NGVI has been unclear. The paper aims to bridge this gap and provide a better understanding. For conjugate likelihoods, it proves an O(1/T) non-asymptotic convergence rate for stochastic NGVI, with complexity comparable to stochastic gradient descent (black-box variational inference). This likely leads to faster convergence in practice due to its better constant dependency. For non-conjugate likelihoods, the paper shows that stochastic NGVI with canonical parameterization optimizes a non-convex objective, making global convergence unlikely without significant advances in optimizing the ELBO using natural gradients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stochastic natural gradient variational inference (NGVI) is a powerful method used to make predictions about unknown variables. Despite its importance, researchers haven’t fully understood how well it works in certain situations. This paper tries to change that by studying how fast NGVI can converge to the correct answer. The results show that for some types of problems, NGVI can quickly get close to the right answer, and this might be because it’s better at finding the best solution than other methods. |
Keywords
» Artificial intelligence » Inference » Stochastic gradient descent
