A new arXiv preprint examines positive-unlabeled (PU) classification when the common SCAR assumption does not hold. The authors study logistic regression approaches, including a cluster-based method and Lasso-regularized variants, and add oversampling to improve performance. The work appears as a cross-listing in cs.AI and cs.LG.
A revised arXiv paper proposes a noise-adaptive approach to conformal inference for classification. Standard conformal methods give prediction sets with guaranteed coverage, but the work addresses how that reliability holds when label or feature noise is present. The method targets marginal coverage guarantees while adjusting to noisy data conditions.
A new arXiv paper introduces GeoTTER, a framework that recasts optimal transport for zero-shot classification tasks. The authors argue that standard approaches are poorly calibrated and inflexible because they depend on fixed cost matrices. GeoTTER instead exploits local geometric structure to improve adaptability.
A new arXiv paper proposes classifying natural language texts along multiple factors using weighted structured patterns such as N-grams, while accounting for heterarchical rather than strictly hierarchical links between those patterns. The authors apply the method to detecting cognitive distortions, framing it as a socially impactful task, and emphasize that the approach keeps the decision process interpretable. The work appears as a cross-listed replacement submission in arXiv cs.AI and cs.LG.
A new arXiv paper introduces a three-term spectral modification of the Hestenes-Stiefel conjugate gradient method, derived using least-squares approximations of a modified quasi-Newton update. The approach aims to retain the algorithm's resistance to jamming while guaranteeing sufficient descent. The authors apply it to a revised robust binary classification model.
Researchers introduce CoGReV, a framework that applies confidence-gated, non-monotonic belief revision to adjust machine learning outputs in phishing website detection. The goal is to reduce false alarms that burden human analysts reviewing classifier decisions, which can otherwise lead to alert fatigue and weaker oversight. The paper appears on arXiv as a version-3 replacement.
A new arXiv paper proves that balanced k-shot sampling, which draws exactly k labeled examples per class, induces an exact and provable degeneracy in a family of small-sample discriminant estimators. The result concerns kernelized linear discriminant methods applied to LLM embeddings, where the within-class scatter operator breaks down under equal per-class sample counts. The finding carries practical consequences for few-shot classification pipelines that rely on embeddings from large language models.
A new study examines how to classify blood cells and their aggregates imaged by deformability cytometry, a high-throughput imaging flow cytometry technique that measures cellular stiffness alongside properties like area and elongation. The researchers compare multi-label and multi-class machine learning approaches for identifying individual cells and clusters within microfluidic channels. The work aims to determine which labeling scheme better captures cases where multiple cell types or aggregates appear in a single measurement.
A newly posted arXiv paper introduces a kernel-based modular discriminant analysis framework targeting the small-sample-size problem in machine learning, where labeled data are scarce due to cost, accessibility, or ethical constraints. The work addresses limitations of existing methods that struggle to perform reliably when training examples are limited.
A systematic evaluation on arXiv examines whether large language models give consistent predictions when used as ordinal classifiers. The researchers ran controlled experiments showing that semantically equivalent changes to prompt organization, such as the ordering of labels and demonstrations, can shift model outputs. The findings highlight reliability concerns for deploying LLMs in ranking and rating tasks.