Update logbook: Reproduction: Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
a74de38 verified Executive summary
We audited all 6 anchored theoretical claims with a standalone, self-contained NumPy/SciPy instrument. Outcome: 0 verified, 6 toy, 0 inconclusive. All audits run on small controlled instances — no GPU, no training, on CPU only. Each claim page carries its pre-registered pass criterion, any protocol delta, the measured numbers, and the negative control where one was run. Claims whose criterion could not discriminate are labelled toy and say so on the page, rather than being reported as verified; where the anchored wording exceeds what the paper text supports, we say that plainly rather than overclaim.
Scope & cost
| Item | Value |
|---|---|
| GPU / compute | None. Pure NumPy/SciPy, CPU only |
| Wall time | CPU-only; not separately timed |
| Feasibility | High. Self-contained audit.py, deps NumPy/SciPy |
<p>Build a reproduction poster with <a href="https://github.com/Chenruishuo/posterly">Chenruishuo/posterly</a> and replace this cell with <code>poster_embed.html</code>.</p>