Summary of Multiclass Transductive Online Learning, by Steve Hanneke et al.
Multiclass Transductive Online Learning
by Steve Hanneke, Vinod Raman, Amirreza Shaeiri, Unique Subedi
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper addresses the problem of multiclass transductive online learning when the number of labels is unbounded. Previous works only considered binary and finite label spaces, but this paper provides a solution by introducing two new dimensions: Level-constrained Littlestone dimension and Level-constrained Branching dimension. The former characterizes online learnability in unbounded label spaces, while the latter determines constant minimax expected mistake-bounds. The authors also improve upon existing multiclass upper bounds by removing the dependence on the label set size and explicitly construct learning algorithms that can handle large or unbounded label spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can learn new things when they’re given information one piece at a time, even if there are many possible answers. This is important because it helps us understand how to make computers smarter and more helpful. The authors come up with some new ideas that help them figure out how well the computer is doing, and they show that these ideas work even when there are lots of possibilities. They also create special algorithms that can handle a huge number of possible answers. |
Keywords
» Artificial intelligence » Online learning
