Summary of Learnability in Online Kernel Selection with Memory Constraint Via Data-dependent Regret Analysis, by Junfan Li et al.
Learnability in Online Kernel Selection with Memory Constraint via Data-dependent Regret Analysis
by Junfan Li, Shizhong Liao
First submitted to arxiv on: 1 Jul 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 paper explores online kernel selection under memory constraints, where the limited memory affects both kernel selection and online prediction procedures. The study investigates the intrinsic relationships between learnability, memory constraint, and data complexity. The authors provide worst-case lower bounds for learning within a small memory budget, showing that learning is impossible in certain scenarios. In contrast, they propose an algorithmic framework offering data-dependent upper bounds relying on two data complexities: kernel alignment and cumulative losses of competitive hypotheses. The proposed algorithms achieve expected upper bounds depending on kernel alignment and smooth loss functions. The results demonstrate the possibility of learning within a small memory budget if the data complexities are sub-linear. Finally, the authors empirically verify their algorithm’s prediction performance on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to choose the best way to learn from online data when you have limited space to store information. It looks at how different things affect each other: how much memory you have, how complex the data is, and how well you can learn from it. The authors show that if you don’t have enough space, learning isn’t always possible. But they also propose a new way of doing online learning that takes into account how hard or easy the data is. They test their method on real datasets and see how well it works. |
Keywords
» Artificial intelligence » Alignment » Online learning
