experiment-design

Plan ML experiments by translating hypotheses into concrete runbooks that define comparison baselines, component-level ablations, datasets, splits, seeds, metrics, and compute budgets. This generates a structured experiment plan and state file for rigorous execution.

Estimate compute budgets by documenting hardware requirements, GPU-hours, storage, and wall time estimates during pre-experiment planning. This ensures resource availability before execution and outputs structured resource estimates alongside ablation plans.

Design ablation studies by planning component-level removals to test specific claims and interactions within your model. The experiment design process structures these ablations alongside baselines and metrics to validate hypotheses rigorously.

Yes, plan execution ordering by scheduling quick validations first, then core experiments, followed by ablations. This staged approach optimizes compute usage and validates assumptions before committing to full-scale hypothesis testing.

Ensure dataset representativeness by verifying coverage across categories with sufficient diversity, and align metrics with motivation by confirming efficiency or claimed benefits are explicitly measured in the experiment plan.

Determine sample sizes and seeds by planning statistical considerations including random seeds, cross-validation folds, and sample size calculations. This ensures reproducible and statistically valid results across all experiment runs.