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The Ethereal
Noise in Classification
October 10, 2020 ยท Declared Dead ยท ๐ Beyond the Worst-Case Analysis of Algorithms
"No code URL or promise found in abstract"
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Authors
Maria-Florina Balcan, Nika Haghtalab
arXiv ID
2010.05080
Category
cs.LG: Machine Learning
Cross-listed
cs.DS,
stat.ML
Citations
15
Venue
Beyond the Worst-Case Analysis of Algorithms
Last Checked
4 months ago
Abstract
This chapter considers the computational and statistical aspects of learning linear thresholds in presence of noise. When there is no noise, several algorithms exist that efficiently learn near-optimal linear thresholds using a small amount of data. However, even a small amount of adversarial noise makes this problem notoriously hard in the worst-case. We discuss approaches for dealing with these negative results by exploiting natural assumptions on the data-generating process.
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