Summary of Derandomizing Multi-distribution Learning, by Kasper Green Larsen et al.
Derandomizing Multi-Distribution Learning
by Kasper Green Larsen, Omar Montasser, Nikita Zhivotovskiy
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); Statistics Theory (math.ST)
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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 explores the challenges and limitations of derandomizing algorithms used in multi-distribution learning, which involves training a single predictor to work well across multiple data distributions. Recent research has shown that near-optimal sample complexity can be achieved with oracle-efficient algorithms for binary loss and finite VC dimension classes. However, these algorithms output randomized predictors, raising the question of whether they can be derandomized to produce deterministic predictors. The authors show that derandomizing multi-distribution learning is computationally hard, even when efficient ERM (empirical risk minimization) is available. On the positive side, they identify a structural condition enabling an efficient black-box reduction to convert existing randomized predictors into deterministic ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make algorithms used in machine learning work better across different types of data. These algorithms are called multi-distribution learning and they try to find one predictor that works well for many kinds of data. Some recent research has shown that these algorithms can be really good at finding the right answer, but only if they use random numbers. The question is: can we make these algorithms work without using random numbers? The authors of this paper show that it’s hard to derandomize these algorithms, which means making them not use random numbers. But they also find a way to make some of these algorithms deterministic, which means they won’t use random numbers. |
Keywords
* Artificial intelligence * Machine learning
