Summary of Group-wise Oracle-efficient Algorithms For Online Multi-group Learning, by Samuel Deng et al.
Group-wise oracle-efficient algorithms for online multi-group learning
by Samuel Deng, Daniel Hsu, Jingwen Liu
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); 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 focuses on online multi-group learning, where a learner must predict well for multiple subpopulations or groups within a large dataset. The groups can be defined by demographics or other attributes. Unlike previous work, this study considers scenarios where the number of groups is too large to enumerate explicitly, so algorithms need to access groups through an optimization oracle. The researchers design algorithms that achieve sublinear regret in various settings, including independent and identically distributed data, adversarial smoothed context distributions, and transductive learning with adversarial noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to predict what people will do based on their age, gender, or other characteristics. This paper looks at how to make predictions for many different groups within a large dataset. The groups can be defined by demographics like age, gender, or even zip code. Unlike previous research, this study considers situations where there are too many groups to count, so algorithms need to find the right groups indirectly. The researchers design new ways to do this that work well in different scenarios. |
Keywords
» Artificial intelligence » Optimization
