Summary of Online Posterior Sampling with a Diffusion Prior, by Branislav Kveton et al.
Online Posterior Sampling with a Diffusion Prior
by Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song
First submitted to arxiv on: 4 Oct 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 The proposed approximate posterior sampling algorithms for contextual bandits with a diffusion model prior can be used to implement exact or approximate methods using the Laplace approximation, which is computationally efficient but cannot describe complex distributions. The algorithm samples from a chain of approximate conditional posteriors, one for each stage of the reverse process, which are estimated in a closed form using the Laplace approximation. This approach inherits the simplicity and efficiency of posterior sampling with a Gaussian prior while being asymptotically consistent and performing well empirically on various contextual bandit problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes new ways to solve complex decision-making problems in real-world situations. Imagine you’re trying to decide which movie to watch based on your past choices, or choosing the best route to take to work based on traffic patterns. This is what’s known as a “contextual bandit” problem, where you need to make decisions based on changing conditions. The researchers developed new methods for solving these problems using special kinds of mathematical models called “diffusion models”. Their approach is simple and efficient, but also powerful enough to handle complex situations. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
