Summary of Information Complexity Of Stochastic Convex Optimization: Applications to Generalization and Memorization, by Idan Attias et al.
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization
by Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper investigates the relationship between memorization and learning in stochastic convex optimization (SCO). The authors quantify memorization using conditional mutual information (CMI) and show that there is a tradeoff between the accuracy of a learning algorithm and its CMI. They demonstrate that this tradeoff can be characterized precisely, answering an open question posed by Livni. The results have implications for generalization bounds based on CMI and the incompressibility of samples in SCO problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning algorithms work together with memorization to get better results. Memorization is like remembering specific details about the data used to train an algorithm. The authors use a special tool called conditional mutual information (CMI) to measure this memorization. They found that there’s a tradeoff between how well an algorithm does and how much it remembers. This means that algorithms can’t just focus on getting good results without considering what they’re actually learning. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Optimization
