# Эксперимент: Full-Likelihood Auxiliary Representation Training (#31)

{
  "worked": true,
  "confidence": 7,
  "verdict": "Built a shared-encoder target classifier plus auxiliary/target domain-odds head trained with the joint full-likelihood objective. The odds identity and Fisher total-variance decomposition passed numerically, with relative error 2e-14 and decomposition error 0. On the tiny synthetic shift, joint training improved 16-target NLL from 0.2763 to 0.2169 and accuracy from 0.9475 to 0.9535 at lambda=0.01, but gains were not consistent at larger target sizes and stronger lambda caused negative transfer.",
  "metrics": {
    "baseline": "n=16: NLL 0.2763, accuracy 0.9475; n=64: NLL 0.0832, accuracy 0.9920; n=256: NLL 0.0230, accuracy 0.9978",
    "idea": "Best joint setting lambda=0.01: n=16 NLL 0.2169, accuracy 0.9535; n=64 NLL 0.0864, accuracy 0.9933; n=256 NLL 0.0268, accuracy 0.9965. Domain accuracy was 0.87-0.90."
  },
  "how_to_run": "python3 experiment.py",
  "files": [
    "experiment.py"
  ],
  "limitations": "Only a small synthetic two-dimensional covariate-shift benchmark was tested, with one seed and no CIFAR/MNIST comparison. FLOPs, multiple-seed confidence intervals, anchor regularization, odds clipping, and comparisons to domain-adversarial or importance-weighted baselines were not tested."
}