Summary of Bayesian Binary Search, by Vikash Singh et al.
Bayesian Binary Search
by Vikash Singh, Matthew Khanzadeh, Vincent Davis, Harrison Rush, Emanuele Rossi, Jesse Shrader, Pietro Lio
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel probabilistic variant of the classical binary search/bisection algorithm is introduced, leveraging machine learning/statistical techniques to estimate the probability density of the search space. Bayesian Binary Search (BBS) modifies the bisection step to split based on probability density rather than the traditional midpoint, allowing for the learned distribution of the search space to guide the search algorithm. BBS can flexibly be performed using supervised probabilistic machine learning techniques or unsupervised learning algorithms. The proposed approach demonstrates significant efficiency gains in both simulated data and a real-world binary search use case, such as probing channel balances in the Bitcoin Lightning Network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to search for things is developed by combining traditional searching with machine learning. Instead of just splitting the search space in half each time like usual, this method uses probability to guide where to split it next. This can be done using different types of machine learning models or statistical methods. Tests show that this new approach works well and can be used in real-world applications, such as finding information on the internet. |
Keywords
» Artificial intelligence » Machine learning » Probability » Supervised » Unsupervised
