Summary of Tamed Langevin Sampling Under Weaker Conditions, by Iosif Lytras and Panayotis Mertikopoulos
Tamed Langevin sampling under weaker conditions
by Iosif Lytras, Panayotis Mertikopoulos
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Numerical Analysis (math.NA); Optimization and Control (math.OC); Probability (math.PR)
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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 The paper addresses the challenge of sampling from distributions that don’t meet traditional smoothness requirements, a common issue in deep learning applications. The authors propose a new sampler tailored to the growth and decay properties of the target distribution, providing guarantees for the Kullback-Leibler divergence, total variation, and Wasserstein distance. The approach leverages log-Sobolev or Poincaré inequalities, local Lipschitz smoothness assumptions, and a taming scheme to efficiently sample from such distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to overcome sampling challenges in deep learning by introducing a new sampler that adapts to the target distribution’s growth and decay. This helps improve our ability to draw meaningful samples from these distributions, which is crucial for many applications. |
Keywords
» Artificial intelligence » Deep learning
