71 lines
5.6 KiB
Org Mode
71 lines
5.6 KiB
Org Mode
:PROPERTIES:
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:ID: cd785954-008e-4b64-957d-25d1dbeac127
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:END:
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#+title: thesis/related-work
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#+filetags: :project: :knowledge: :related-work:
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Chapter 2 of the BGU MSc thesis on ROLL. Background and prior work. Target: 15–20 pages.
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Parent: [[id:6294e2be-6189-4473-b363-a1dd9a75fb9b][thesis]]
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** Guidelines
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- Narrows from broad background toward the specific gap ROLL fills
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- Reader should feel ROLL is inevitable by the end of the chapter
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- NP section belongs at the END — it is the narrowest prior work and bridges directly to Ch. 3
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** Section Structure
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| # | Title | Label | Placement | Length | Status |
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|---|-------|-------|-----------|--------|--------|
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| 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) |
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| 2.2 | ROC Curves and AUC Optimization | TBD | middle | ~3–4 pp | Not yet written; cover AUC surrogates, pairwise ranking |
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| 2.3 | Partial AUC and TPR@FPR Methods | TBD | middle | ~3 pp | Not yet written; key point: no end-to-end backprop |
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| 2.4 | Kernel Density Estimation | sec:kde-background | middle | ~3–4 pp | Not yet written; forward-pointer to sec:roll-kde (Ch. 3) |
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| 2.5 | Learning with Noisy Labels | sec:noisy-labels | after KDE, before NP | ~3–4 pp | Outline in file; not yet written |
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| 2.6 | Connection to Neyman-Pearson | sec:neyman-pearson | END | ~2–3 pp | Moved from Ch. 3; needs writing (currently a stub) |
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** Gotchas
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- sec:imbalanced-tpr-fpr and sec:neyman-pearson are \Cref-referenced in Ch. 3 (sec:problem-formulation) — do not rename these labels
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- KDE section (sec:kde-background) must end with a forward pointer to sec:roll-kde in Ch. 3 — established convention from project brain root
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- Ch. 3 KDE section opens with a back-reference to sec:kde-background — keep this pairing consistent
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- 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
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- AUC section should distinguish whole-curve AUC from operating-point-specific TPR@FPR — this distinction motivates the partial AUC / TPR@FPR section
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- 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
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- 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
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- Noisy labels bibtex entries staged in impl/research ** Noisy Labels Literature; move to thesis.bib when writing this section
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** Noisy Labels Literature (sec:noisy-labels)
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*** wu2025preserving — AUC Fairness + Noisy Protected Groups (ICML 2025)
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- cite key: =wu2025preserving= — ADDED to thesis.bib
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- arxiv: 2505.18532
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- *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
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- *Groups*: demographic/sensitive attributes (gender, race) — separate from class labels; noise = mislabeled demographics, not mislabeled predictions
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- *Datasets*: Adult (tabular, socioeconomic), FF++ (image, deepfake detection)
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- *Baselines*: MinimaxFairAUC (Yang et al. 2023), DAW-FDD (Ju et al. 2024), standard AUC fairness methods
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- *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)
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- *Thesis relevance*: cite in sec:noisy-labels; connects to ROLL's robustness argument in Ch. 4
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*** xie2024wsauc — Weakly Supervised AUC, Unified Partial AUC (IEEE TPAMI 2024)
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- cite key: =xie2024wsauc= — ADDED to thesis.bib
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- arxiv: 2305.14258 / doi: 10.1109/TPAMI.2024.3357814
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- Authors: Zheng Xie, Yu Liu, Hao-Yuan He, Ming Li, Zhi-Hua Zhou
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- *Not group fairness* — about robustness of AUC under weak/imperfect supervision
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- *Covers four weak supervision regimes*: noisy label learning, positive-unlabeled (PU) learning, multi-instance learning, semi-supervised learning
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- *Key insight*: all four regimes can be unified as "minimizing AUC risk on contaminated sets"
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- *Novel metric*: rpAUC (reversed partial AUC) — used as a robust surrogate training objective when labels are contaminated; theoretical consistency with true AUC proven
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- *Framework*: WSAUC — universal solution via empirical rpAUC maximization
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- *Datasets/baselines*: PDF is image-based (scanned), could not extract; likely standard benchmarks (CIFAR/MNIST class)
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- *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
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*** zhang2023doubly — Doubly Robust AUC vs Noisy + Adversarial Samples (KDD 2023)
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- cite key: =zhang2023doubly= — *NOT YET ADDED to thesis.bib; need unpaywalled copy first*
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- doi: 10.1145/3580305.3599316 — ACM paywalled, no arXiv preprint found
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- *Not about group fairness* — robustness of AUC to (1) noisy class labels and (2) adversarial perturbations
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- *Method (DRAUC)*: self-paced learning + adversarial training within AUC optimization; theoretical upper bound on adversarial AUC risk; alternating stochastic descent
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- *Datasets/baselines*: unknown — full text inaccessible
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- *Thesis relevance*: cite in sec:noisy-labels alongside wu2025preserving — brackets noisy-label problem from two sides (noisy demographics vs. noisy class labels)
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- *ACTION NEEDED*: obtain unpaywalled PDF to verify datasets/baselines and add bibtex entry
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