Summary of Fast Rates For Bandit Pac Multiclass Classification, by Liad Erez et al.
Fast Rates for Bandit PAC Multiclass Classification
by Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper tackles multiclass PAC learning with bandit feedback, where inputs are classified into one of K possible labels and feedback is limited to whether or not the predicted labels are correct. The authors design a novel learning algorithm for the agnostic (ε,δ)-PAC version of the problem, achieving sample complexity bounds of O(poly(K) + 1/ε^2) log(|H|/δ). This improves upon existing bounds and resolves an open question in realizable PAC learning. The algorithm utilizes stochastic optimization techniques and Frank-Wolfe updates to minimize a log-barrier potential, making it computationally efficient when access is granted to an ERM oracle over H. Key concepts include model names (Frank-Wolfe updates), methods (stochastic optimization), datasets (), tasks (PAC learning), and relevant subfields (agnostic PAC). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a way for machines to learn from feedback when they’re not sure which of several possible answers is correct. The goal is to design an efficient algorithm that can accurately classify inputs into one of many categories, even with limited information. The researchers create a new learning method that works well in this scenario and improves upon previous results. This breakthrough has implications for how machines learn from feedback and could be used in real-world applications. |
Keywords
* Artificial intelligence * Optimization
