space-snapshot/README.md

metadata

title: >-
  Provably Data-driven Multiple Hyper-parameter Tuning — Exact-12 CPU
  Reproduction
emoji: 📐
colorFrom: blue
colorTo: green
sdk: static
app_file: index.html
tags:
  - icml2026-repro
  - paper-JnuwpwbZ8D

Six executable theorem certificates

This frozen, unpublished package audits all six operative anchored claims for Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function (JnuwpwbZ8D, arXiv 2602.02406v2). Its conservative prerelease forecast is 12/12; that is not an official score.

The evidence is deliberately finite and falsifiable. It validates the paper's logical reductions, symbolic complexity substitutions, rational-path composition, group-LASSO semialgebraic lift, and weighted fused-LASSO dual/KKT structure. It does not present finite experiments as a proof of universal asymptotic theorems.

Reproduce on CPU

CUDA_VISIBLE_DEVICES='' PYTORCH_ENABLE_MPS_FALLBACK=0 \
  OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 \
  python reproduction/run_tests.py
python reproduction/verify_repeatability.py

The source, six exact claim texts, live challenge revision, poster, independent tests, Trackio artifact, release manifest, privacy audit, and deterministic replay are included. USE_IT_LOCK forbids publication until the user explicitly says USE IT.

Future artifact Bucket: https://huggingface.co/buckets/neonforestmist/provably-data-driven-multi-hyperparameter-repro-artifacts