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Characterizing Online Learnability Role of VC dimension - Finite VC dimension is not sufficient, because of thresholds on a line. • VC dimension focuses on labeling a set . But we need to consider la…
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Learning and Incentives
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- 1 Intro
- 2 Learning and Learnability One of the goals of theory of ML
- 3 Learnability for Today's World
- 4 Learnability Q1. What concepts can be learned in presence of strategic and adversarial behavior? → Lessons for todays world from decade of efforts for understanding
- 5 Tutorial Overview
- 6 Stochastic (Offline) Settings Usage Example: Learning to detect natural phenomenon or fixed distribution objects, eg, trees, animals, etc.
- 7 Formal Setup: Stochastic setting
- 8 Alternative Setup: (Stochastic) Offline Learning
- 9 What characterizes offline learnability?
- 10 VC Dimension Example
- 11 Why VC Dimension?
- 12 Stochastic (Offline) Settings Usage Examples Controlling the content quality, face adversarial manipulation of future instances and have to updated
- 13 Formal Setup: Online vs Stochastic Setting
- 14 Characterizing Online Learnability Role of VC dimension - Finite VC dimension is not sufficient, because of thresholds on a line. • VC dimension focuses on labeling a set . But we need to consider la…
- 15 Characterization of Online Learnability
- 16 Algorithms based on Littlestone Dimension
- 17 Solution Concepts
