| { |
| "id": "ml14-root", |
| "requirements": "A credible experiment comparing split-conformal, Mondrian conformal, CQR-style, and optionally naive-quantile prediction intervals for ~90% target coverage on heteroscedastic regression: methods are implemented, executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally with coverage-gap-focused analysis.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative conformal variants (e.g., jackknife+, locally adaptive) that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml14-code", |
| "requirements": "The conformal and baseline interval-construction conditions are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml14-code-methods", |
| "requirements": "The submission implements multiple distinct conditions \u2014 typically including split conformal, a Mondrian/group-adaptive variant, and a CQR-style or naive-quantile baseline \u2014 as separate code paths, not a single shared interval formula.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml14-code-conformal-splits", |
| "requirements": "Conformal methods use explicit train/calibration/test separation (or equivalent cross-fit logic) so calibration quantiles are computed on data not used to fit base regressors.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml14-code-datasets", |
| "requirements": "The submission uses multiple datasets (including at least one heteroscedastic synthetic dataset) and prepares regression targets correctly.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml14-exec", |
| "requirements": "Execution outputs interval-quality metrics for each condition.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml14-exec-metrics", |
| "requirements": "Execution produces a metrics artifact containing numeric coverage gap and mean interval width (or equivalents) for each implemented condition on at least one dataset.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml14-exec-seeds", |
| "requirements": "Reported metrics are aggregated over multiple random seeds per (condition, dataset) cell with a dispersion estimate. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml14-exec-conditional", |
| "requirements": "Execution computes a conditional-coverage diagnostic (e.g., worst-bin miscoverage across several bins) for the conformal methods.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml14-results", |
| "requirements": "Quantitative analysis addresses H1/H2/H3 directionally and discusses trade-offs between coverage, conditional validity, and interval efficiency.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml14-result-h1", |
| "requirements": "The submission compares coverage gap of adaptive methods (Mondrian, CQR-style) against plain split conformal per dataset and conveys whether adaptive methods are meaningfully closer to the nominal coverage \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml14-result-h2", |
| "requirements": "The submission evaluates whether CQR-style intervals achieve mean interval width comparable to or narrower than plain split conformal on most datasets and conveys an H2 outcome.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml14-result-h3", |
| "requirements": "The submission compares worst-bin miscoverage for Mondrian conformal vs plain split conformal and conveys whether Mondrian yields a meaningful improvement in conditional coverage (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml14-result-writeup", |
| "requirements": "The README or writeup describes methods and datasets, reports key metric values (coverage gap, interval width, worst-bin miscoverage), conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (finite seeds, binning choice, synthetic-to-real transfer). No strict word-count requirement.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
|
|
