Summary of Online Learning Of Halfspaces with Massart Noise, by Ilias Diakonikolas et al.
Online Learning of Halfspaces with Massart Noise
by Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Statistics Theory (math.ST); 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 This paper explores online learning in the presence of Massart noise. The study assumes an adversarial context selection, but with unknown probability, the presented label disagrees with the true label. The researchers focus on γ-margin linear classifiers and propose a computationally efficient algorithm that achieves a mistake bound of ηT + o(T). This bound is qualitatively tight for efficient algorithms, as even in offline settings, achieving error better than η requires super-polynomial time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are trying to learn new things from the internet while dealing with noisy and unfair information. They assume that the information is selected unfairly, but they don’t know how often it’s wrong. The researchers look at a special type of computer program called linear classifiers and create an efficient way to make predictions. Their results show that even when learning offline, achieving good accuracy requires a lot of time and effort. |
Keywords
» Artificial intelligence » Online learning » Probability
