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:ID: cd785954-008e-4b64-957d-25d1dbeac127
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#+title: thesis/related-work
#+filetags: :project: :knowledge: :related-work:

Chapter 2 of the BGU MSc thesis on ROLL. Background and prior work. Target: 15–20 pages.
Parent: [[id:6294e2be-6189-4473-b363-a1dd9a75fb9b][thesis]]

** Guidelines

- Narrows from broad background toward the specific gap ROLL fills
- Reader should feel ROLL is inevitable by the end of the chapter
- NP section belongs at the END — it is the narrowest prior work and bridges directly to Ch. 3

** Section Structure

| # | Title | Label | Placement | Length | Status |
|---|-------|-------|-----------|--------|--------|
| 2.1 | Imbalanced Classification and TPR/FPR Objective | sec:imbalanced-tpr-fpr | START | ~3–4 pp | Moved from Ch. 3; needs rewrite (see TODO comments in file) |
| 2.2 | ROC Curves and AUC Optimization | TBD | middle | ~3–4 pp | Not yet written; cover AUC surrogates, pairwise ranking |
| 2.3 | Partial AUC and TPR@FPR Methods | TBD | middle | ~3 pp | Not yet written; key point: no end-to-end backprop |
| 2.4 | Kernel Density Estimation | sec:kde-background | middle | ~3–4 pp | Not yet written; forward-pointer to sec:roll-kde (Ch. 3) |
| 2.5 | Learning with Noisy Labels | sec:noisy-labels | after KDE, before NP | ~3–4 pp | Outline in file; not yet written |
| 2.6 | Connection to Neyman-Pearson | sec:neyman-pearson | END | ~2–3 pp | Moved from Ch. 3; needs writing (currently a stub) |

** Gotchas

- sec:imbalanced-tpr-fpr and sec:neyman-pearson are \Cref-referenced in Ch. 3 (sec:problem-formulation) — do not rename these labels
- KDE section (sec:kde-background) must end with a forward pointer to sec:roll-kde in Ch. 3 — established convention from project brain root
- Ch. 3 KDE section opens with a back-reference to sec:kde-background — keep this pairing consistent
- sec:imbalanced-tpr-fpr arc (per TODO in file): accuracy gameable under imbalance → class weighting standard fix → no direct operating-point control → therefore need TPR@FPR directly
- AUC section should distinguish whole-curve AUC from operating-point-specific TPR@FPR — this distinction motivates the partial AUC / TPR@FPR section
- sec:noisy-labels added 2026-07-13; NP moved to 2.6 (was 2.5); sec:neyman-pearson label is UNCHANGED so Ch. 3 \Cref references still work without modification
- sec:noisy-labels should be forward-referenced from Ch. 4 poisoning experiment; bridge: ROLL's robustness is a structural consequence of the distributional objective, not a design goal
- Noisy labels bibtex entries staged in impl/research ** Noisy Labels Literature; move to thesis.bib when writing this section

** Noisy Labels Literature (sec:noisy-labels)

*** wu2025preserving — AUC Fairness + Noisy Protected Groups (ICML 2025)
- cite key: =wu2025preserving= — ADDED to thesis.bib
- arxiv: 2505.18532
- *Fairness notion*: gap between group-level AUC and overall AUC (both intra-group and inter-group AUC); constraint is that no group's AUC deviates far from aggregate
- *Groups*: demographic/sensitive attributes (gender, race) — separate from class labels; noise = mislabeled demographics, not mislabeled predictions
- *Datasets*: Adult (tabular, socioeconomic), FF++ (image, deepfake detection)
- *Baselines*: MinimaxFairAUC (Yang et al. 2023), DAW-FDD (Ju et al. 2024), standard AUC fairness methods
- *What's novel*: first empirical analysis of noisy protected-group label impact on AUC fairness; DRO framework bounding TV distance between clean/noisy distributions; CLIP-based noise detection (no extra training)
- *Thesis relevance*: cite in sec:noisy-labels; connects to ROLL's robustness argument in Ch. 4

*** xie2024wsauc — Weakly Supervised AUC, Unified Partial AUC (IEEE TPAMI 2024)
- cite key: =xie2024wsauc= — ADDED to thesis.bib
- arxiv: 2305.14258 / doi: 10.1109/TPAMI.2024.3357814
- Authors: Zheng Xie, Yu Liu, Hao-Yuan He, Ming Li, Zhi-Hua Zhou
- *Not group fairness* — about robustness of AUC under weak/imperfect supervision
- *Covers four weak supervision regimes*: noisy label learning, positive-unlabeled (PU) learning, multi-instance learning, semi-supervised learning
- *Key insight*: all four regimes can be unified as "minimizing AUC risk on contaminated sets"
- *Novel metric*: rpAUC (reversed partial AUC) — used as a robust surrogate training objective when labels are contaminated; theoretical consistency with true AUC proven
- *Framework*: WSAUC — universal solution via empirical rpAUC maximization
- *Datasets/baselines*: PDF is image-based (scanned), could not extract; likely standard benchmarks (CIFAR/MNIST class)
- *Thesis relevance*: cite in sec:noisy-labels; rpAUC as a robust surrogate is conceptually adjacent to ROLL's KDE-based surrogate — both address the "how do you optimize AUC reliably" question

*** zhang2023doubly — Doubly Robust AUC vs Noisy + Adversarial Samples (KDD 2023)
- cite key: =zhang2023doubly= — *NOT YET ADDED to thesis.bib; need unpaywalled copy first*
- doi: 10.1145/3580305.3599316 — ACM paywalled, no arXiv preprint found
- *Not about group fairness* — robustness of AUC to (1) noisy class labels and (2) adversarial perturbations
- *Method (DRAUC)*: self-paced learning + adversarial training within AUC optimization; theoretical upper bound on adversarial AUC risk; alternating stochastic descent
- *Datasets/baselines*: unknown — full text inaccessible
- *Thesis relevance*: cite in sec:noisy-labels alongside wu2025preserving — brackets noisy-label problem from two sides (noisy demographics vs. noisy class labels)
- *ACTION NEEDED*: obtain unpaywalled PDF to verify datasets/baselines and add bibtex entry