Summary of Deterministic Apple Tasting, by Zachary Chase and Idan Mehalel
Deterministic Apple Tasting
by Zachary Chase, Idan Mehalel
First submitted to arxiv on: 14 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 paper presents a new deterministic algorithm for online classification with apple tasting feedback, where the learner only receives feedback when predicting a 1. The authors show that a hypothesis class is learnable if and only if it is deterministically learnable, confirming a conjecture in [Raman, Subedi, Raman, Tewari-24]. They also provide a widely-applicable deterministic apple tasting learner and quantify the mistake bound with an O(sqrt(L(H)T log T)) guarantee. The authors demonstrate that their algorithm can achieve this bound for some classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to learn from feedback on certain types of problems. It’s like trying to guess what kind of apple someone likes, and only getting feedback when you get it right. Previously, all known algorithms for this problem were random, so it was unknown if there was a way to do it without guessing. The authors show that yes, it is possible to make a non-random algorithm that works well. They also provide a detailed formula for how well their algorithm does. |
Keywords
* Artificial intelligence * Classification
