| { |
| "id": "ml06-root", |
| "requirements": "A credible experiment studying whether adaptive learning-rate schedules improve logistic-regression convergence on binary classification: schedule conditions are implemented as distinct update rules, runs are executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Equivalent schedules (e.g., a different decay parameterization) should be credited when the underlying mechanism is clearly the intended one.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml06-code", |
| "requirements": "The logistic-regression training framework and learning-rate schedule variants are implemented in a way that allows a meaningful convergence comparison.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml06-code-schedules", |
| "requirements": "The submission implements a fixed-learning-rate baseline and multiple adaptive schedules (e.g., step-decay, exponential-decay, cosine-annealing, cosine-warm-restarts, or comparable alternatives) as distinct update rules, not duplicated code paths.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml06-code-model", |
| "requirements": "A binary logistic-regression objective (sigmoid + log-loss) is trained via iterative gradient-based optimization with per-epoch control, enabling convergence-epoch tracking.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml06-code-data", |
| "requirements": "The submission uses multiple datasets (including at least one sklearn built-in or synthetic classification set), performs train/validation/test splitting, and applies feature scaling consistently.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml06-exec", |
| "requirements": "Execution produces convergence and quality metrics with reasonable seed coverage.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml06-exec-metrics", |
| "requirements": "Execution produces a machine-readable metrics artifact with numeric convergence-epochs (or equivalent convergence measure) and validation log-loss for each implemented (dataset, condition) cell.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml06-exec-seeds", |
| "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml06-exec-auc", |
| "requirements": "Execution also reports a final predictive-quality metric (e.g., test ROC-AUC) for each condition on at least one dataset, so readers can check that convergence-speed gains do not collapse classification quality.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml06-results", |
| "requirements": "The analysis evaluates all three hypotheses and discusses the speed-vs-quality trade-off.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml06-result-h1", |
| "requirements": "The submission compares cosine-style schedules against the fixed-rate baseline on convergence-epochs across the evaluated datasets and conveys whether cosine schedules converge meaningfully faster \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml06-result-h2", |
| "requirements": "The submission reports the spread of final validation log-loss among the adaptive schedules and conveys whether the best-vs-worst gap is small (i.e., final-quality differences are minor once converged).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml06-result-h3", |
| "requirements": "The submission compares cosine-warm-restarts against exponential-decay (or equivalent schedules) on convergence speed and final ROC-AUC and conveys the qualitative outcome for H3.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml06-result-writeup", |
| "requirements": "The README or writeup describes setup and key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as convergence-threshold choice, seed count, or dataset scope. 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 |
| } |
|
|
