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thesis/todos

Writing — chapters

TODO Write Ch. 1 (Introduction)

  • Hook: imbalance + noise are everywhere, standard losses fail silently
  • Gap: no existing method does AUC optimization + noise robustness together
  • ROLL's claim: distributional objective, robustness as structural consequence
  • Close with chapter roadmap

TODO Write Ch. 5 (Discussion)

  • Explain WHY ROLL-AOC is noise robust (KDE distributional fit vs isolated gradients)
  • Explain WHY Bank Marketing fails (open question, speculate honestly)
  • Explain WHY GCE/MAE fail under asymmetric imbalanced noise
  • Compare mechanism with LibAUC (same objective, different path)

TODO Write Ch. 6 (Conclusion)

  • Summary: differentiable AUC surrogate via score-distribution fitting
  • Key result: noise robustness validated on KEEL (5/10/20%) and CIFAR-10N (≤40%)
  • Limitations: Bank Marketing, 99-pass cost, bandwidth scheduler hyperparameter
  • Future work: learned bandwidth, partial-AUC variant, multi-class extension

TODO Finish Ch. 4 (Results) — remaining stubs

  • Write Summary section (sec:experiments:summary) — key points noted in comments
  • Decide: write or drop Ablation Study and Sensitivity Analysis sections
  • Fill all \cite{TODO} placeholders in experiments.tex

TODO Review Ch. 4 (Results) end-to-end

  • Check narrative flow after noise section was added
  • Confirm KEEL/CIFAR-10N/large-dataset table interpretations are accurate
  • Verify roll-tpr90 is correctly described as secondary/special-case throughout

Writing — front/back matter

TODO Write English abstract

TODO Write Hebrew abstract

TODO Fill Acknowledgements (Main_pages/acknowledgements.tex)

Writing — Ch. 2 & 3 loose ends

TODO Verify alpha notation w.r.t. FPR — eq:fpr-cdf writes FPR(τ) = F̂₀(τ) and threshold as τ = F̂₀⁻¹(α), but standard FPR = P(score > τ | y=0) = 1−F₀(τ) requires τ = F̂₀⁻¹(1−α). Check for sign/notation inconsistency throughout method.tex and appendix_gauss_grad.tex.

TODO Add graphs of CDF tracking for gaussian, KDE, roll.

TODO Cite in sec:related-work (Ch. 2):

  • Benchmarking Deep AUROC Optimization (arXiv 2203.14177) — cite in 2.2 as survey of AUROC losses; finding: composite losses best overall, barrier hinge best under noise
  • LibAUC pAUCLoss / SOPAs (arXiv 2203.01505) — cite in 2.3 as partial AUC baseline
  • "Closing the Approximation Gap" (arXiv 2512.01213, Dec 2025) — cite in 2.3 as recent concurrent work on partial AUC
  • "When AUC meets DRO" — cite in 2.3 as DRO-based partial AUC approach

Experiments — open questions

TODO Debug banking dataset — results unexpectedly low (roll-aoc 0.511 vs GCE 0.746), investigate root cause

TODO Check if LibAUC pAUCLoss (one-way partial AUC / SOPAs) should be added as baseline

Final pass

TODO Build full PDF and proofread end-to-end

TODO Check all cross-references (\Cref, \label) are consistent after restructuring

TODO Verify Hebrew ToC entries match English section titles

DONE [A] Rerun keel noised experiments — data exists in results-final/keel-noise-05/10/20

DONE Restructure thesis per mentor guidelines (2026-08-17)

  • [x] Focus framing on noise resistance + AUC solution (primary story)
  • [x] Minimize single-point optimization coverage (brief mention only)
  • [x] Ch. 2 restructured: ROC/AUC → Noisy Labels → KDE → Imbalanced → NP
  • [x] Ch. 3 restructured: AUC as primary objective, ROLL-AOC promoted
  • [x] Noise results table added to Ch. 4