papers/zoo-poai-consensus/sections/13-quality-scoring-functions.tex

\section{Quality Scoring Functions}


\begin{lstlisting}[language=python,caption=Quality Scoring Implementation]
def score_inference(attestation, samples):
    correct = sum(1 for s in samples
                  if verify_output(s))
    accuracy = correct / len(samples)
    latency_penalty = max(0,
        (attestation.latency - TARGET) / TARGET)
    return accuracy * (1 - 0.1 * latency_penalty)

def score_training(attestation, val_before, val_after):
    improvement = val_after - val_before
    cost = attestation.flops / 1e15
    return improvement * math.exp(-0.1 * cost)

def score_extraction(experience, library):
    novelty = 1 - max_similarity(experience, library)
    applicability = test_on_benchmarks(experience)
    correctness = verify_experience(experience)
    return novelty * applicability * correctness
\end{lstlisting}