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@@ -26,9 +26,32 @@ BGU MSc thesis on the ROLL method (Rate Optimized Likelihood Loss). See [[id:d23
- Chapters added via =\input{content/<chapter>/<chapter>}= with a matching =\addtocontents{tocheb}= line
- Build engine: pdflatex (uses =\usepackage[utf8x]{inputenc}=, not xelatex)
- Math notation: data points are bold (=\mathbf{x}_i=); batches are =\mathcal{B}_0= / =\mathcal{B}_1=
- KDE background (general technique) in Ch. 2 (=sec:kde-background=); Ch. 3 back-references it; Ch. 2 ends with forward pointer to =sec:roll-kde=
- KDE background (general technique) in Ch. 2 (=sec:kde-background=); Ch. 3 back-references it
- =sec:imbalanced-tpr-fpr= and =sec:neyman-pearson= live in Ch. 2; Ch. 3 =sec:problem-formulation= \Cref's them — do not rename
** Mentor Direction (2026-08-17)
Primary narrative focus agreed with mentor:
- *Main story*: noise resistance and AUC optimization — this is the thesis core
- *Hero method*: =roll-aoc= (AUC variant) — primary contribution
- *Primary competitor*: =libauc-auroc= (Yuan et al. AUCMLoss + PESG) — already implemented
- *Single-point optimization* (=roll-tpr90=): touch on briefly as secondary result
- *Pacing*: get to the differentiating, strong results quickly — don't bury the lede
** Baselines (all in =impl/src/roll.py= + =impl/experiments/keel/_base.py=)
| Experiment name | Class | What it is |
|---|---|---|
| =bce-weighted= | BCE + pos_weight | Standard imbalance fix |
| =libauc-auroc= | =libauc_auc_loss= + PESG | Yuan et al. 2021 compositional AUC — primary AUC competitor |
| =gce-0.7= | =gce_loss= | Generalized Cross-Entropy (noise-robust) |
| =mae= | =mae_loss= | MAE loss (noise-robust) |
| =focal-loss= | =focal_loss= | Focal Loss |
| =roll-aoc= | =kernelized_roll_aoc= | *ROLL AUC variant — hero method* |
| =roll-tpr90= | =kernelized_roll_tpr= | ROLL TPR@FPR variant — secondary result |
Note: =libauc-auroc= requires PESG optimizer (not Adam) — already wired up in impl.
** Chapter Structure
See subnodes for per-chapter guidelines and section details.
@@ -50,7 +73,7 @@ See subnodes for per-chapter guidelines and section details.
- =nix develop= devShell includes python3+sympy — use it to verify math claims, never check mentally
- KDE backward pass uses inverse-function-theorem approach deliberately (user must explain it verbally)
- Correct gradient of threshold w.r.t. negative-class score: σ'(τ−x_i) / Σ_j σ'(τ−x_j) — sympy-verified
- **(NIX BUILD) New .tex files must be =git add='ed before =nix build=.** Nix flakes use git to enumerate source files; untracked files are invisible to the build. Symptom: silent LaTeX failure — TOC, LoF, and LoT all appear empty in the output PDF. Fix: =git add <new-file>=, then rebuild.
- *(NIX BUILD) New .tex files must be =git add='ed before =nix build=.* Nix flakes use git to enumerate source files; untracked files are invisible to the build. Symptom: silent LaTeX failure — TOC, LoF, and LoT all appear empty in the output PDF. Fix: =git add <new-file>=, then rebuild.
** Key Files
@@ -63,9 +86,11 @@ See subnodes for per-chapter guidelines and section details.
- [[id:c4a0d60f-49d3-4185-8c5b-f8a5a10a2ccc][thesis/introduction]] — Ch. 1 guidelines: structure, length, what to include
- [[id:cd785954-008e-4b64-957d-25d1dbeac127][thesis/related-work]] — Ch. 2 section structure, placement markers, label conventions
- [[id:0543ac39-af9b-40fd-9973-e60576a20695][thesis/method]] — Ch. 3 section status table, derivation gotchas
- [[id:59f84838-71a9-4aab-a494-8e6bea2080fb][thesis/experiments]] — Ch. 4 guidelines: datasets, baselines, metrics
- [[id:c41756b5-fd5f-456e-ab2b-18f27db7d61d][thesis/experiments]] — Ch. 4 data sources, key results, section status
- [[id:9b50caf2-d259-4848-b7f5-22a9288e8ba2][thesis/discussion]] — Ch. 5–6 guidelines: failure modes, future work
- [[id:151159e8-1105-4a84-af9c-72d31c5e0660][thesis/todos]] — project TODOs
- [[id:bd98ede2-d6ec-4e93-8d10-4800fe528ff7][thesis/citations]] — sources justifying implementation choices (architectures, baselines)
- [[id:2c598296-d2f0-4333-9cdd-3625f8375e03][thesis/work-history/recent]] — session work log
* todo