# Эксперимент: Risk-Fitted Shrinkage Gate (#1351)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a PyTorch Risk-Fitted Shrinkage Gate with monotone piecewise-linear spline knot values, analytic divergence, and minibatch SURE optimization. The divergence formula matched finite differences (3.1520 vs 3.1501), and repeated SURE differed from realized risk by only -0.0215 per coordinate. On held-out normal-mean data, the fitted gate improved risk over identity and fixed soft-thresholding in both sparse and dense regimes, so the claimed adaptive bias-variance effect was observed in the toy setting.",
  "metrics": {
    "baseline": "Sparse: identity risk 1.0106, fixed soft-threshold risk 0.2293; dense: identity 1.0033, fixed soft-threshold risk 1.0800.",
    "idea": "Sparse: risk-fitted gate risk 0.1081; dense: risk-fitted gate risk 0.8029. Learned knot values were strongly attenuated in the sparse regime and substantially closer to identity in the dense regime."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 risk_gate_experiment.py",
  "files": [
    "risk_gate_experiment.py",
    "results.json"
  ],
  "limitations": "Only a small synthetic normal-mean denoising experiment was tested; no CIFAR-10 CNN/MLP integration, validation accuracy, calibration, FLOPs, activation sparsity, or full noise-scale EMA implementation was evaluated. The experiment uses one fixed seed and a small number of repeated SURE draws, so downstream-task generalization remains unverified."
}