Summary of Ai-sampler: Adversarial Learning Of Markov Kernels with Involutive Maps, by Evgenii Egorov and Ricardo Valperga et al.
Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps
by Evgenii Egorov, Ricardo Valperga, Efstratios Gavves
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 new method for training transition kernels in Markov chain Monte Carlo algorithms is proposed, aiming to achieve efficient sampling and good mixing properties. This approach parameterizes and trains kernel transitions using reversible neural networks, ensuring detailed balance by construction. The training procedure minimizes the total variation distance between the stationary distribution of the chain and the empirical data distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to make Markov chains work better for complicated probability distributions. It uses special kinds of neural networks that ensure the chains stay balanced and don’t get stuck in certain places. This helps the algorithm sample efficiently and accurately from these complex distributions. |
Keywords
» Artificial intelligence » Probability
