| { |
| "id": "ml13-root", |
| "requirements": "A credible experiment studying kernel-choice effects (RBF, polynomial, Matern-3/2, Matern-5/2, or equivalents) for Gaussian Process regression on synthetic noisy functions: conditions are implemented, execution covers multiple datasets with repeated seeds, and results address H1/H2/H3 directionally using test NLL as the primary metric.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative kernels or dataset dimensions that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml13-code", |
| "requirements": "The GP kernel conditions and synthetic datasets are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml13-code-kernels", |
| "requirements": "The submission implements multiple kernel conditions \u2014 typically including RBF, a polynomial kernel, and one or more Matern kernels \u2014 as distinct GP configurations with comparable noise handling.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml13-code-datasets", |
| "requirements": "The submission generates synthetic regression datasets with explicit noise (both a low-dimensional and a higher-dimensional case preferred) and creates train/test splits.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml13-code-setup", |
| "requirements": "Experimental setup controls are consistent across kernels (same split protocol, seed handling, and optimizer restart policy), enabling fair kernel comparison.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml13-exec", |
| "requirements": "Execution logs primary and secondary metrics for each kernel and dataset.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml13-exec-metrics", |
| "requirements": "Execution produces a metrics artifact containing numeric test NLL and RMSE (or equivalents) for every implemented (kernel, dataset) pair.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml13-exec-seeds", |
| "requirements": "Reported metrics are aggregated over multiple random seeds per (kernel, dataset) cell, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 8.3333, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Hyperparameter Tuning" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml13-results", |
| "requirements": "Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml13-result-h1", |
| "requirements": "The submission reports per-dataset mean test-NLL ranking and conveys whether smooth kernels (RBF, Matern-5/2) tend to be best \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml13-result-h2", |
| "requirements": "The submission compares the polynomial-kernel NLL against the best kernel and conveys whether polynomial is meaningfully worse on at least one dataset (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml13-result-h3", |
| "requirements": "The submission compares kernel rankings by test NLL and RMSE on each dataset and conveys whether rankings can differ between these two metrics (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml13-result-writeup", |
| "requirements": "The README or writeup describes implementation choices, dataset generation, metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, kernel parameterization, seed/compute constraints). 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 |
| } |
|
|
