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Sample Complexity of Learning Rest of this talk: Fix learning parameters a = 0.01,49 = 0.01 Learning Thresholds: Possible with a number of samples n = 0(1) independent of T
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Privacy, Stability, and Online Learning
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- 1 Intro
- 2 Motivating Questions
- 3 Non-Private Classification
- 4 Example: Learning 1-Dim Thresholds Space of examples X - 7 - 1....I
- 5 Sample Complexity of Learning Rest of this talk: Fix learning parameters a = 0.01,49 = 0.01 Learning Thresholds: Possible with a number of samples n = 0(1) independent of T
- 6 Sample Complexity of Private Learning
- 7 Characterizing Private Sample Complexit
- 8 Characterizing Private Learnability
- 9 Online Learning / Littlestone Dimension
- 10 Mistake Bounded Learning vs. DP
