Summary of Efficient Online Set-valued Classification with Bandit Feedback, by Zhou Wang et al.
Efficient Online Set-valued Classification with Bandit Feedback
by Zhou Wang, Xingye Qiao
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposed Bandit Class-specific Conformal Prediction (BCCP) method addresses limitations in conformal prediction when using bandit feedback. Traditional conformal prediction methods require fully observed label information, which is not feasible in online learning with bandit feedback. BCCP overcomes this challenge by offering coverage guarantees on a class-specific granularity. The approach trains the model and makes set-valued inferences through stochastic gradient descent, overcoming sparsely labeled data issues and generalizing reliability to online decision-making environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal prediction is like a special kind of box that can tell you which label is most likely true. Usually, this box needs to see all the labels to work well. But what if it only gets to look at some of them? That’s the problem in online learning where you don’t get to see all the answers. The new method, called BCCP, solves this by being more careful and making better predictions even with limited information. |
Keywords
» Artificial intelligence » Online learning » Stochastic gradient descent
