Summary of Bayesian Online Natural Gradient (bong), by Matt Jones et al.
Bayesian Online Natural Gradient (BONG)
by Matt Jones, Peter Chang, Kevin Murphy
First submitted to arxiv on: 30 May 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 This paper presents a novel approach to sequential Bayesian inference using variational Bayes (VB). The key innovation is an optimization technique that avoids adding the KL term to regularize to the prior in each time step. Instead, the method performs a single step of natural gradient descent starting from the prior predictive distribution. The authors prove that this approach recovers exact Bayesian inference when the model is conjugate. They also develop efficient deterministic approximations for Gaussian and sub-family variational distributions. Empirical results show that their method outperforms other online VB methods, including neural networks, while controlling for computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to do something called Bayesian inference using something called variational Bayes (VB). It’s like trying to figure out what will happen next in a sequence of events. The authors found a clever trick that makes it easier and faster than before. They show that this approach works really well when the problem is connected to something familiar, but it can also handle more complicated cases. By using this new method, they were able to do things better and faster than previous methods. |
Keywords
» Artificial intelligence » Bayesian inference » Gradient descent » Optimization
