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Classroom Contents
Can Non-Convex Optimization Be Robust?
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- 1 Intro
- 2 Why is non-convex optimization "easy"?
- 3 Locally optimizable functions
- 4 What happens when assumptions fail?
- 5 Robust non-convex optimization with perturbed objective
- 6 Motivation: Empirical Risk vs. Population Risk.
- 7 Idea: Smoothing
- 8 Properties of Smoothing
- 9 Ideas of the Lower Bound
- 10 Matrix Completion
- 11 Semi-Random Adversary
- 12 Counter Examples
- 13 Preprocessing
- 14 Summary
- 15 Open Problems
