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Update logbook: Reproduction: Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
a74de38 verified - claim-1-c1-theorem-4-1-establishes-a-general-first-order-logic-framework-giving-p
- claim-2-c2-theorem-5-1-bounds-the-pseudo-dimension-of-piecewise-polynomial-traini
- claim-3-c3-theorem-6-1-extends-the-framework-to-the-bi-level-validation-loss-sett
- claim-4-c4-theorem-7-2-shows-that-when-the-optimal-parameter-path-theta-x-alpha
- claim-5-c5-theorem-8-1-provides-the-first-learnability-guarantee-for-weighted-gro
- claim-6-c6-theorem-8-2-derives-a-pdim-l-o-d-2-bound-for-weighted-fused-lasso
- conclusion
- executive-summary
- 1.26 kB