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Classroom Contents
Wasserstein Distributionally Robust Optimization - Theory and Applications in Machine Learning
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- 1 Intro
- 2 Decision-Making under Uncertainty
- 3 Data-Driven Decision-Making
- 4 Nominal Distribution
- 5 Estimation Errors
- 6 Wasserstein Distance
- 7 Stability Theory
- 8 Distributionally Robust Optimization (DRO)
- 9 Wasserstein DRO
- 10 Gelbrich Bound (p = 2)
- 11 Strong Duality
- 12 Piecewise Concave Loss
- 13 Main Takeaways
- 14 Warst-Case Risk for p = 1
- 15 Computing the Gelbrich Bound
- 16 Piecewise Quadratic Lass
- 17 Classification
- 18 Regression
- 19 Maximum Likelihood Estimation
- 20 Minimum Mean Square Error Estimation
