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Double Descent in Linear regression
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Classroom Contents
From Classical Statistics to Modern Machine Learning
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- 1 Intro
- 2 Supervised ML
- 3 Generalization bounds
- 4 Classical U-shaped generalization curve
- 5 Does interpolation overfit?
- 6 Interpolation does not overfit even for very noisy data
- 7 Deep learning practice
- 8 Generalization theory for interpolation?
- 9 A way forward?
- 10 Interpolated k-NN schemes
- 11 Interpolation and adversarial examples
- 12 "Double descent" risk curve
- 13 what is the mechanism?
- 14 Double Descent in Linear regression
- 15 Occams's razor
- 16 The landscape of generalization
- 17 where is the interpolation threshold?
- 18 Optimization under interpolation
- 19 SGD under interpolation
- 20 The power of interpolation
- 21 Learning from deep learning: fast and effective kernel machines
- 22 Important points
- 23 From classical statistics to modern ML
