| { |
| "id": "ml22-root", |
| "requirements": "A credible experiment studying active-learning query strategies (random, uncertainty, margin, QBC, expected error reduction, or equivalents) for logistic regression: strategies are implemented as distinct code paths, execution covers multiple datasets with repeated seeds under a fixed budget, and results address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative acquisition functions or base classifiers that preserve the scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml22-code", |
| "requirements": "Active-learning strategies and shared logistic-regression pipeline are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml22-code-strategies", |
| "requirements": "The submission implements multiple distinct query conditions \u2014 typically random plus several of {uncertainty, margin, QBC, expected error reduction} \u2014 with genuinely different acquisition logic.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml22-code-loop", |
| "requirements": "There is a pool-based active-learning loop with an initial labeled seed set, iterative querying, model retraining/update, and stopping at a defined label budget.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml22-code-datasets", |
| "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable) with consistent train/pool/test handling for all strategies.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml22-exec", |
| "requirements": "Execution emits budget-aware performance metrics.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml22-exec-metrics", |
| "requirements": "Execution produces a machine-readable metrics artifact containing numeric accuracy-at-budget and area-under-learning-curve (or equivalents) for each implemented strategy on at least one dataset.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml22-exec-seeds", |
| "requirements": "Each reported (strategy, dataset) result is aggregated over multiple random seeds, with a dispersion measure. 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": "ml22-exec-budget-curve", |
| "requirements": "The run logs performance across multiple budget checkpoints so early-budget accuracy and AULC are computable from recorded traces.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml22-results", |
| "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml22-result-h1", |
| "requirements": "The submission compares non-random strategies to random sampling at the final budget and conveys whether informative strategies meaningfully outperform random \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml22-result-h2", |
| "requirements": "The submission reports per-dataset AULC rankings and conveys whether expected-error-reduction (or a comparable principled strategy) is at least competitive, never falling below random (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml22-result-h3", |
| "requirements": "The submission compares QBC vs uncertainty sampling at an early-budget checkpoint per dataset and conveys a qualitative verdict (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml22-result-writeup", |
| "requirements": "The README or report describes setup, reports key numeric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (runtime approximations, seed count, dataset scope, strategy sensitivity). 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 |
| } |
|
|
