Summary of Smoothed Online Classification Can Be Harder Than Batch Classification, by Vinod Raman et al.
Smoothed Online Classification can be Harder than Batch Classification
by Vinod Raman, Unique Subedi, Ambuj Tewari
First submitted to arxiv on: 24 May 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 investigates online classification under smoothed adversaries, where an adversary draws examples from a distribution with bounded density relative to a known base measure. The authors build upon previous works showing that smoothed online learning is equivalent to batch learning in certain settings, but they also show that unbounded label spaces can make smoothed online classification harder than iid batch classification. They construct a hypothesis class that is learnable in the batch setting but not in the smoothed online setting, highlighting a condition for PAC learnability being sufficient for smoothed online learnability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to classify things online when there’s an “adversary” trying to trick you. The adversary draws examples from a special kind of distribution that has limits. The authors show that this can be tricky, especially if there are lots of different labels. They create an example where something that works well in one situation doesn’t work as well in another. |
Keywords
» Artificial intelligence » Classification » Online learning
