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Research map / Label-efficient and robust learning / Knowledge distillation

Iterative Classroom Teaching

AAAI 2019 · Label-efficient and robust learning › Knowledge distillation · approach: Teacher properties and robustness · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Choose shared examples using the leading eigenvector of the learners' weighted error matrixClassroom teaching for online projected gradient descent learners · Iterative classroom teachingteaching examples: \mathcal{O} \br{N \log \frac{1}{\epsilon}} -> \mathcal{O} \br{k \log \frac{1}{\epsilon}}Helped
Use noisy estimates of learner states to construct examplesClassroom teaching · Noisy student-state observationsteaching rounds: \mathcal{O}\br{\br{\log \frac{1}{1 - \frac{\alpha_{\mathrm{min}}}{k}}}^{-1} \log \frac{1}{\epsilon}}Helped
Estimate time-varying learning rates from past valuesClassroom teaching · Time-varying learning ratesexpected teaching rounds: \mathcal{O}\br{\br{\log \frac{1}{1 - \frac{\beta_{\mathrm{min}} \bar \alpha_{\mathrm{min}}}{k}}}^{-1} \log \frac{1}{\epsHelped
Partition learners into homogeneous groups based on learning rateCT · Synthetic learners with diverse learning ratesteacher examples to convergenceMixed
Partition learners by prior knowledge or hypothesis typeCT · Synthetic learners with diverse initial states; butterfly and moth classificationstudent workloadHelped
Use dynamic rather than static example magnitudeClassroom teaching · Iterative classroom teachingsample complexityMixed

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