Summary of Binary Search with Distributional Predictions, by Michael Dinitz et al.
Binary Search with Distributional Predictions
by Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Aidin Niaparast, Sergei Vassilvitskii
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 research paper, the authors introduce a new framework for combining traditional worst-case algorithms with modern machine learning. They focus on binary search, where the prediction itself is a distribution rather than a single point. The authors show that using distributions can lead to better performance and provide an algorithm with query complexity O(H(p) + log η), which is essentially optimal up to constants. This work has implications for classical problems like computing optimal binary search trees given a distribution over target keys. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners, this paper is about finding the best way to search through a list of things when we’re not sure what we’re looking for. Traditionally, we make a single guess and then adjust based on the result. But what if our prediction is actually a range of possibilities? The authors explore this idea by studying binary search with distributional predictions. They show that using distributions can be more effective than traditional methods and provide an algorithm to achieve better results. |
Keywords
* Artificial intelligence * Machine learning
