Summary of Reciprocal Learning, by Julian Rodemann et al.
Reciprocal Learning
by Julian Rodemann, Christoph Jansen, Georg Schollmeyer
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A wide range of machine learning algorithms can be viewed as instances of a single paradigm called reciprocal learning, which encompasses active learning, multi-armed bandits, and self-training. These algorithms do not only learn from data but also alter training data based on the current model fit. Reciprocal learning is introduced as a generalization using decision theory language, allowing for the study of convergence conditions. The key to convergence lies in guaranteeing that reciprocal learning contracts satisfy the Banach fixed-point theorem. This leads to linear-rate convergence to an approximately optimal model under mild assumptions on the loss function, provided predictions are probabilistic and sample adaptation is non-greedy and either randomized or regularized. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms can be grouped into a single concept called reciprocal learning. This includes different types of learning like active learning, bandits, and self-training. These algorithms learn from data but also change the data they’re learning from based on their current performance. We’ve introduced this idea using decision theory and used it to understand when these algorithms will eventually reach an optimal model. The key is making sure these algorithms “contract” in a specific way, which allows them to converge quickly. |
Keywords
» Artificial intelligence » Active learning » Generalization » Loss function » Machine learning » Self training
