roam/thesisintroduction.org

thesis/introduction

• Thesis Narrative (corrected 2026-08-29)
• Guidelines
• Structure
• Gotchas

Chapter 1 of the BGU MSc thesis on ROLL. Written last, after Ch. 2–4 are stable. Parent: thesis

Thesis Narrative (corrected 2026-08-29)

The thesis is about learning under label noise and class imbalance, solved via direct AUC optimization. The hero method is ROLL-AoC. The surprise finding is implicit noise robustness — not a design goal, but a structural consequence of distributional score fitting.

Guidelines

• Target length: 4–6 pages
• Write last — contributions list and roadmap must match what the thesis actually delivers
• Structure: Hook → Problem statement → Contributions → Roadmap
• Do NOT explain the method — introduction is a promise, not a delivery

Structure

1. Hook (~1–2 paragraphs) — concrete scenario where noisy labels + imbalance combine badly. Good candidates from experiments: ChestX-ray14 (NLP-extracted labels, IR≈7.5:1), credit card fraud (IR≈577:1), CIFAR-10N (crowdworker noise, 40% worst-annotator). Make the reader feel the problem before naming it.

2. Problem statement (~1–2 paragraphs) — three interlocking failures:

   • Standard losses (BCE, focal) fail under imbalance (accuracy wrong metric) AND noise (mislabeled samples produce large isolated gradient outliers)
   • Noise-robust losses (GCE, MAE) stay per-sample — no AUC connection
   • AUC-optimizing methods (LibAUC) use pairwise surrogates that lose direct connection to the curve

   Gap: no single method simultaneously optimizes AUC, handles imbalance structurally, and is robust to label noise.

3. Contributions (bulleted list):

   • ROLL: general framework for differentiable ROC-based optimization via score distribution fitting
   • Three instantiations: Gaussian, Beta, KDE (KDE is primary)
   • ROLL-AoC: direct numerical AUC integration via 99 per-FPR evaluations — distinct from pairwise surrogates
   • Gradient balance property: updates invariant to class-size ratio
   • Gradient locality property: gradient mass concentrates near operating threshold
   • Empirical: implicit noise robustness — ROLL-AoC outperforms noise-designed losses (GCE, MAE) at 20% noise, consistent across tabular (KEEL), image (CIFAR-10N), medical imaging (ChestX-ray14)
4. Roadmap (~1 paragraph) — one sentence per chapter, matching final chapter titles exactly.

Gotchas

• Roadmap must exactly match final chapter titles — write it last
• Don't use the inseparability / cascade-classifier tangent from the old draft; it dilutes the argument
• Don't frame as "operating point optimization" — the pivot is AUC + label noise
• The noise robustness is implicit (emergent), not a design objective — this is the surprising result to sell