Updated results, hebrew, abstract, intro, etc.

Main_pages/abstract.tex

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 Real datasets used to train models are often plagued by
 label noise, where a fraction of training labels are incorrect. Standard per-sample losses such as binary cross-entropy are sensitive to mislabeled examples and optimize per-sample accuracy rather than ranking quality, making them misaligned with the Area Under the ROC Curve (AUC), the standard metric for imbalanced classification. Noise-robust losses (GCE, MAE) address the sensitivity problem but remain per-sample and inherit the same metric mismatch. Existing AUC-maximizing methods improve on this by directly targeting ranking quality, but rely on pairwise surrogate losses that optimize for AUC indirectly.
 
-In this work we introduce \textsc{ROLL} (Rate Optimized Likelihood Loss), a framework for differentiable ROC-curve optimization via output score distribution fitting. The primary instantiation, \textsc{ROLL}-AoC, approximates AUC as the mean TPR sampled across a range of FPR thresholds. This loss utilizes a Kernel Density Estimation to approximate the distribution of output scores at each epoch, and optimizes it directly via gradient descent with a custom backward pass. Although noise robustness is not an explicit design objective, \textsc{ROLL}-AoC exhibits strong implicit resistance to label noise.
+In this work we introduce \textsc{ROLL} (Rate Optimized Likelihood Loss), a framework for differentiable ROC-curve optimization via output score distribution fitting. The primary instantiation, \textsc{ROLL}-AOC, approximates AUC as the mean TPR sampled across a range of FPR thresholds. This loss utilizes a Kernel Density Estimation to approximate the distribution of output scores at each epoch, and optimizes it directly via gradient descent with a custom backward pass. Although noise robustness is not an explicit design objective, \textsc{ROLL}-AOC exhibits strong implicit resistance to label noise.
 
-We evaluate on twelve KEEL benchmark datasets under label noise, CIFAR-10N under real crowdworker annotation noise, three large-scale tabular datasets, and NIH ChestX-ray14 with organic NLP-extracted label noise. Across all benchmarks, \textsc{ROLL}-AoC is competitive under clean labels and leads under high noise rates, outperforming dedicated noise-robust losses.
+We evaluate on twelve KEEL benchmark datasets under label noise, CIFAR-10N under real crowdworker annotation noise, three large-scale tabular datasets, and NIH ChestX-ray14 with organic NLP-extracted label noise. Across all benchmarks, \textsc{ROLL}-AOC is competitive under clean labels and leads under high noise rates, outperforming dedicated noise-robust losses.