Summary of Stochastic Localization Via Iterative Posterior Sampling, by Louis Grenioux et al.
Stochastic Localization via Iterative Posterior Sampling
by Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus
First submitted to arxiv on: 16 Feb 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 In this paper, researchers explore stochastic localization techniques for sampling from unnormalized target densities. They introduce a new framework called Stochastic Localization via Iterative Posterior Sampling (SLIPS), which uses Markov chain Monte Carlo estimation to obtain approximate samples of the dynamics and, as a byproduct, samples from the target distribution. The authors demonstrate the effectiveness of SLIPS on various benchmarks of multi-modal distributions, including Gaussian mixtures in increasing dimensions, Bayesian logistic regression, and a high-dimensional field system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special math techniques to help computers generate random numbers that follow specific patterns or shapes. They create a new way called Stochastic Localization via Iterative Posterior Sampling (SLIPS) that uses a special type of computer simulation to get these random numbers. The authors show how this technique works well for different types of data, such as mixtures of normal distributions and complex systems from statistical mechanics. |
Keywords
* Artificial intelligence * Logistic regression * Multi modal
