If X is unambiguous we write ˆ µ = ˆµ (X ). A summary of notation in tabular form is given in the supplement.

If X is unambiguous we write ˆ µ = ˆµ (X ). A summary of notation in tabular form is given in the supplement.

Modern applications in sensitive domains such as biometrics and medicine frequently require the use of non-decomposable loss functions such as precision@k, F-measure etc. Compared to point loss functions such as hinge-loss, these offer much more fine grained control over prediction, but at the same time present novel challenges in terms of algorithm design and analysis. In this work we initiate a study of online learning techniques for such non-decomposable loss functions with an aim to enable incremental learning as well as design scalable solvers for batch problems. To this end, we propose an online learning framework for such loss functions. Our model enjoys several nice properties, chief amongst them being the existence of efficient online learning algorithms with sublinear regret and online to batch conversion bounds.

Our proposed algorithm can also be used to minimize the sum of ranked range loss, which also lacks efficient solvers.

Skin cancer is among the most prevalent and life-threatening diseases worldwide, with early detection being critical to patient outcomes. This work presents a hybrid machine and deep learning-based approach for classifying malignant and benign skin lesions using the SLICE-3D dataset from ISIC 2024, which comprises 401,059 cropped lesion images extracted from 3D Total Body Photography (TBP), emulating non-dermoscopic, smartphone-like conditions. Our method combines vision transformers (EVA02) and our designed convolutional ViT hybrid (EdgeNeXtSAC) to extract robust features, employing a segmentation-assisted classification pipeline to enhance lesion localization. Predictions from these models are fused with a gradient-boosted decision tree (GBDT) ensemble enriched by engineered features and patient-specific relational metrics. To address class imbalance and improve generalization, we augment malignant cases with Stable Diffusion-generated synthetic lesions and apply a diagnosis-informed relabeling strategy to harmonize external datasets into a 3-class format. Using partial AUC (pAUC) above 80 percent true positive rate (TPR) as the evaluation metric, our approach achieves a pAUC of 0.1755 -- the highest among all configurations. These results underscore the potential of hybrid, interpretable AI systems for skin cancer triage in telemedicine and resource-constrained settings.

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after meals, is a critical indicator of progression toward type 2 diabetes in prediabetic and healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial area under the curve (PAUC). Predicting PAUC in advance based on a person's diet and activity level and explaining what affects postprandial blood glucose could allow an individual to adjust their lifestyle accordingly to maintain normal glucose levels. In this paper, we propose GlucoLens, an explainable machine learning approach to predict PAUC and hyperglycemia from diet, activity, and recent glucose patterns. We conducted a five-week user study with 10 full-time working individuals to develop and evaluate the computational model. Our machine learning model takes multimodal data including fasting glucose, recent glucose, recent activity, and macronutrient amounts, and provides an interpretable prediction of the postprandial glucose pattern. Our extensive analyses of the collected data revealed that the trained model achieves a normalized root mean squared error (NRMSE) of 0.123. On average, GlucoLense with a Random Forest backbone provides a 16% better result than the baseline models. Additionally, GlucoLens predicts hyperglycemia with an accuracy of 74% and recommends different options to help avoid hyperglycemia through diverse counterfactual explanations. Code available: https://github.com/ab9mamun/GlucoLens.

Modern applications in sensitive domains such as biometrics and medicine frequently require the use of non-decomposable loss functions such as precision@k, F-measure etc. Compared to point loss functions such as hinge-loss, these offer much more fine grained control over prediction, but at the same time present novel challenges in terms of algorithm design and analysis. In this work we initiate a study of online learning techniques for such non-decomposable loss functions with an aim to enable incremental learning as well as design scalable solvers for batch problems. To this end, we propose an online learning framework for such loss functions. Our model enjoys several nice properties, chief amongst them being the existence of efficient online learning algorithms with sublinear regret and online to batch conversion bounds. Our model is a provable extension of existing online learning models for point loss functions.