:PROPERTIES:
:ID: c41756b5-fd5f-456e-ab2b-18f27db7d61d
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#+title: thesis/experiments
#+filetags: :project: :knowledge: :experiments:

Chapter 4 of the BGU MSc thesis on ROLL. Parent: [[id:6294e2be-6189-4473-b363-a1dd9a75fb9b][thesis]]

** Data Sources

- =results-final/keel/= — 12 KEEL datasets × 8 methods × 5 episodes, clean labels
- =results-final/keel-noise-05/10/20/= — same 12 datasets × 8 methods × 10 episodes, asymmetric noise
- =results-final/cifar10n/= — 3 noise levels × 7 methods × 3 episodes; no roll-tpr90
- =results-final/bank-marketing/=, =creditcard/=, =homecredit/= — 5 episodes each, all methods
- Each directory contains per-dataset =auc.csv= files (already aggregated from pkl)

** Key Results

*** KEEL Noise Experiment (primary thesis result)
Mean AUC across all 12 KEEL datasets at increasing asymmetric label-noise rates:

| Method    | 0%    | 5%    | 10%   | 20%   | Drop 0→20 |
|-----------|-------|-------|-------|-------|-----------|
| roll-aoc  | 0.880 | 0.871 | 0.862 | 0.847 | −0.033    |
| bce-w     | 0.872 | 0.869 | 0.862 | 0.843 | −0.029    |
| focal     | 0.879 | 0.868 | 0.857 | 0.842 | −0.037    |
| asym.     | 0.892 | 0.872 | 0.870 | 0.833 | −0.059    |
| gce-0.7   | 0.886 | 0.870 | 0.865 | 0.831 | −0.056    |
| libauc    | 0.882 | 0.859 | 0.854 | 0.809 | −0.073    |
| mae       | 0.875 | 0.878 | 0.866 | 0.804 | −0.071    |
| roll-tpr  | 0.843 | 0.786 | 0.771 | 0.773 | −0.070    |

*ROLL-AOC is best at 20% noise* despite being mid-pack at 0%.

*** CIFAR-10N (image noise) — updated 2026-09-02
Results from =results-final/cifar10n/auc.csv= (n=3 episodes, all 7 methods).
Regenerated via =scripts/regen_cifar10n_summaries.py= after fixing cuda→cpu map_location.

| Noise | roll-aoc | libauc | gce   | mae   | asym  | bce   | focal |
|-------|----------|--------|-------|-------|-------|-------|-------|
| clean | 0.980    | 0.979  | 0.976 | 0.974 | 0.973 | 0.975 | 0.973 |
| aggre | 0.932    | 0.931  | 0.930 | 0.929 | 0.927 | 0.927 | 0.922 |
| worse | 0.819    | 0.810  | 0.811 | 0.810 | 0.810 | 0.806 | 0.809 |

ROLL-AOC best at all noise levels. At aggre, leads LibAUC by 0.001 and GCE by 0.002.
At worse, all baselines cluster 0.806–0.811; ROLL leads GCE by 0.008.
Degradation (clean→worse): ROLL-AOC −0.161 (least); others −0.163 to −0.169.

*** Large Datasets
- Credit Card Fraud: roll-tpr90 best (0.974), roll-aoc close (0.968); both beat all baselines
- Home Credit: roll-aoc best (0.712), libauc very close (0.711)
- Bank Marketing: ROLL FAILS — roll-aoc 0.511, roll-tpr90 0.438; GCE best (0.746). Open question.

** Gotchas

- "Label-noise experiment" is NOT a poisoning attack — it's asymmetric noise injection (flip pos→neg)
- keel-noise runs use 10 episodes; clean keel uses 5. Mixing is fine for averages.
- libauc-auroc missing 2 episodes for glass2 in clean KEEL (n=58 not 60) — known gap
- cifar10n has no roll-tpr90 variant; table excludes it
- =scripts/regen_cifar10n_summaries.py= originally only remapped mps→cpu, not cuda→cpu. Fixed 2026-09-02.
- Bank Marketing failure cause unknown; do not claim ROLL is universally superior on large tabular data

** Section Structure

| Section | Label | Status |
|---------|-------|--------|
| Datasets | sec:experiments:data_sets | Done |
| Performance Measures | sec:experiments:performance_measures | Done |
| Configurations | sec:experiments:configurations | Done |
| KEEL Results | sec:results:keel | Done — table filled |
| CIFAR-10N Results | sec:results:cifar10n | Done — updated 2026-09-02, all 7 methods |
| Large Datasets Results | sec:results:large | Done — table filled |
| Label-Noise Robustness on KEEL | sec:experiments:noise | Done — table added |
| Ablation Study | sec:experiments:ablation | Stub — future work |
| Sensitivity Analysis | sec:experiments:sensitivity | Stub — future work |
| Summary | sec:experiments:summary | TODO — key points noted in comments |