Summary of Bandits with Abstention Under Expert Advice, by Stephen Pasteris et al.
Bandits with Abstention under Expert Advice
by Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster
First submitted to arxiv on: 22 Feb 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 proposed CBA algorithm significantly improves upon classical Exp4 by exploiting the assumption that one action, corresponding to abstention from play, has no reward or loss on every trial. This approach can be viewed as the aggregation of confidence-rated predictors when the learner has the option of abstention. The authors achieve bounds on the expected cumulative reward for general confidence-rated predictors, with a novel reward bound in the special case of specialists that significantly improves previous bounds. As an example application, the algorithm is implemented to learn unions of balls in a finite metric space, reducing runtime from quadratic to almost linear. Preliminary experiments show CBA outperforming existing bandit algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers worked on a problem where they had to make good choices based on advice from experts. They came up with an idea called the CBA algorithm that helps make better decisions by assuming one option has no effect. This is like combining many predictions when you have the choice not to participate. The team got better results than before and also found a new way to do this for certain types of problems. They even tested their idea on learning shapes in space, making it faster and more efficient. So far, their approach seems to work better than other methods. |
