Summary of Structured Prediction in Online Learning, by Pierre Boudart (di-ens et al.
Structured Prediction in Online Learning
by Pierre Boudart, Alessandro Rudi, Pierre Gaillard
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 presents a theoretical and algorithmic framework for structured prediction in online learning settings. The authors introduce an algorithm that generalizes optimal algorithms from supervised learning, achieving the same excess risk upper bound even when data is not independently identically distributed (i.i.d.). Additionally, they propose a second algorithm designed specifically for non-stationary data distributions, including adversarial data. The paper bounds the stochastic regret of this algorithm in terms of the variation of data distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to make predictions about complex structures online. It’s like trying to predict what someone will say next based on past conversations. The researchers show that their method is just as good as the best methods for predicting simple things, even when the data is not perfectly consistent. They also develop a special algorithm for handling unexpected changes in the patterns of the data. This helps ensure that predictions are still accurate even when new information comes to light. |
Keywords
» Artificial intelligence » Online learning » Supervised
