Lab: Reproducing a Result, the ML Reproducibility Checklist

• Apply the NeurIPS Reproducibility Program's own checklist to Lab 8's experiment: exact hyperparameters, data preprocessing steps, random seeds, and software/hardware versions.
• Simulate a typically under-documented paper by deliberately withholding checklist items, and observe a fresh reproduction attempt fail as a result.
• Fill in every checklist item and confirm the same reproduction attempt now succeeds, matching the original reported result within expected variability.
• Explain, using real data from the NeurIPS report, how common and how consequential missing reproducibility information actually is in published machine learning research.

running-and-reporting-a-real-experiment produced a real result and a written report. This lab asks a harder, more uncomfortable question about that result: could someone else, given only what was written down, actually reproduce it? The NeurIPS Reproducibility Program's own real, peer-reviewed report, published in JMLR, documents this exact problem at scale, with real, collected data from real submitted machine learning papers, not as a hypothetical concern but as a measured, common failure.

Nothing about why reproducibility matters, or the general case for describing experiments precisely, is re-derived here; that argument already exists in graduate-studies/research-statistics's own coding-for-experimentation-and-describing-experiments. This lab applies the specific, real checklist the NeurIPS program developed, machine-learning-specific items a general experimentation checklist does not name explicitly, hyperparameter search ranges, exact random seeds, exact software versions, to Lab 8's own experiment directly.

The checklist, applied to Lab 8's own experiment

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NeurIPS Reproducibility Checklist items relevant to this arc's experiment:

[ ] All training details (hyperparameters, learning rate, iterations)
[ ] Number of random seeds used, and how variability was reported
[ ] Exact train/validation/test split methodology
[ ] Exact data preprocessing steps (including the normalization
    statistics source — see dataset-work-splits-leakage-and-honest-
    evaluation's own leakage discussion)
[ ] Software versions (NumPy version, Python version)
[ ] Hardware used, if runtime or timing is reported
[ ] A link to, or inclusion of, the actual code

Step 1 — simulating a typically under-documented paper

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"UNDER-DOCUMENTED" METHODS SECTION (deliberately incomplete, matching
the NeurIPS report's own documented pattern of common omissions):

  "We trained a regularized linear model on the housing dataset and
  achieved a held-out accuracy of 0.847, outperforming a baseline by
  0.038."

Missing: the regularization STRENGTH (lambda), the exact random
seed(s), the exact preprocessing steps, and the exact baseline
definition — all real, common omissions the NeurIPS report documents
across actual submitted papers, not omissions invented for this lab.

Step 2 — a fresh reproduction attempt, given ONLY the under-documented description

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def attempt_reproduction_from_underdocumented_description():
    # A fresh implementation, written by someone who has ONLY read the
    # methods section above, with no access to Lab 7/Lab 8's actual code
    X, y = load_real_dataset()
    X_train, y_train, X_test, y_test = arbitrary_split(X, y, seed=42)  # guessed seed
    w = ridge_fit(add_bias_column(X_train), y_train, lam=1.0)          # guessed lambda
    accuracy = evaluate_full(lambda X: sigmoid(add_bias_column(X) @ w) >= 0.5, X_test, y_test)["accuracy"]
    return accuracy

def test_reproduction_fails_without_full_checklist():
    reproduced_accuracy = attempt_reproduction_from_underdocumented_description()
    original_accuracy = 0.847  # from running-and-reporting-a-real-experiment
    assert abs(reproduced_accuracy - original_accuracy) > 0.02, \
        "a reproduction attempt guessing at missing hyperparameters and split methodology should NOT closely match the original"

Step 3 — the same attempt, with EVERY checklist item now filled in

plaintext
def attempt_reproduction_with_full_checklist():
    X, y = load_real_dataset()
    # Every value below now comes DIRECTLY from Lab 6/Lab 7/Lab 8's own
    # recorded configuration, not a guess
    X_train, y_train, X_test, y_test = correct_split(X, y, seed=EXACT_SEED_FROM_LAB6)
    train_mean, train_std = X_train.mean(axis=0), X_train.std(axis=0)
    X_train_norm = (X_train - train_mean) / train_std
    X_test_norm = (X_test - train_mean) / train_std
    w = ridge_fit(add_bias_column(X_train_norm), y_train, lam=EXACT_LAMBDA_FROM_LAB7)
    accuracy = evaluate_full(lambda X: sigmoid(add_bias_column(X) @ w) >= 0.5, X_test_norm, y_test)["accuracy"]
    return accuracy

def test_reproduction_succeeds_with_full_checklist():
    reproduced_accuracy = attempt_reproduction_with_full_checklist()
    original_accuracy = 0.847
    assert abs(reproduced_accuracy - original_accuracy) < 0.005, \
        "with every checklist item specified exactly, reproduction should match the original closely"

Step 4 — what the real NeurIPS report actually found

text
The NeurIPS Reproducibility Program's own report documents, from real
submitted papers, that missing information of exactly this kind
(hyperparameter values, random seeds, preprocessing details) was a
common, measured cause of failed reproduction attempts, motivating the
checklist's inclusion as a formal part of the submission process at a
major machine learning venue, not a suggestion offered as optional
best practice.

• "Reporting a final accuracy number is sufficient documentation, since the number itself is what matters." Step 2's own failed reproduction attempt demonstrates directly why this is false: the number alone, without the exact configuration that produced it, cannot be independently verified or built upon by anyone else, which is the real, practical cost missing documentation imposes.
• "Reproducibility problems in machine learning are rare, isolated incidents, not a systemic pattern." The NeurIPS Reproducibility Program's own real, collected data from actual submitted papers documents this as a common, measured pattern, significant enough that a major venue built a formal checklist into its submission process specifically to address it.
• "Once the checklist items are filled in, reproduction should match the original result exactly, not just closely." Step 3's test checks for a close match, not an exact one; real randomness (different hardware, minor floating-point differences, a different random-number-generator implementation across environments) can still produce small variation even with every checklist item specified correctly, which is a different, narrower concern from the large, checklist-driven gap Step 2 demonstrates.

This lab applies the NeurIPS Reproducibility Program's own real, JMLR-published checklist to running-and-reporting-a-real-experiment's result: simulating a typically under-documented paper by withholding specific configuration details, showing a fresh reproduction attempt fails as a direct, measured consequence, then filling in every checklist item and confirming the same attempt now succeeds. The gap between these two attempts is not hypothetical, it reflects a real, documented pattern the NeurIPS report found across actual submitted machine learning papers, which is why a major venue built this checklist into its formal submission process rather than leaving reproducibility to informal convention.