Summary of Majority-of-three: the Simplest Optimal Learner?, by Ishaq Aden-ali et al.
Majority-of-Three: The Simplest Optimal Learner?
by Ishaq Aden-Ali, Mikael Møller Høgsgaard, Kasper Green Larsen, Nikita Zhivotovskiy
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a long-standing problem in learning theory: developing an optimal algorithm for PAC learning in the realizable setting, where ERM (empirical risk minimization) falls short. The breakthrough came years ago with Hanneke’s solution, but it’s complex and involves many ERM classifiers trained on specific data subsets. This work focuses on a simpler alternative: returning the majority vote of three ERM classifiers. The authors show that this algorithm achieves the optimal in-expectation error bound, surpassing a single ERM classifier. Additionally, they prove a near-optimal high-probability error bound and hypothesize that further analysis will confirm its optimality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a tricky problem in learning theory that’s been around for decades. It’s about finding the best way to learn from data when we’re trying to get something specific right (like recognizing pictures of dogs). A smart person named Hanneke already figured out how to do this, but their solution is complicated. This new paper looks at a simpler approach: combining what three different “experts” think are the most likely answers. The experts are actually just simple versions of themselves that have looked at different pieces of data. Amazingly, this combination approach does just as well as Hanneke’s original one in terms of making accurate predictions. It also works really well most of the time, even when things get a little tricky. |
Keywords
* Artificial intelligence * Probability
