arXiv:2607. 23575v1 Announce Type: cross Abstract: Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered.
arXiv:2606. 07599v1 Announce Type: cross Abstract: Ordinal Regression (OR) aims to predict target values with inherent order, underpinning critical applications across diverse domains, from recommender systems to computer vision.
arXiv:2607. 08103v1 Announce Type: new Abstract: Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption.
arXiv:2608.20999v1 Announce Type: new Abstract: Multimodal LLMs apply the language model interface to visual inputs, where ordinal regression tasks such as age estimation, image quality assessment, a...
arXiv:2607. 08109v1 Announce Type: new Abstract: We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning.
We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively leverages all samples in a batch, it typically ignores the inherent ordering among rank labels.
Large language models (LLMs) used for ordinal classification exhibit positional bias, where changes in label order, demonstration order, and demonstration placement affect predictions. Systematic experiments across ten frontier LLMs, eight prompt/task/model factors, and five datasets reveal that all models are sensitive to these positional sources, and that accuracy and stability often diverge. Various correction methods, including pointwise, pairwise, and listwise inference, do not reliably mitigate the bias, though a comparison-based listwise approach shows the best overall balance yet varies across models and bias types.
Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation.
arXiv:2607. 18465v1 Announce Type: new Abstract: Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video.
arXiv:2603.06399v2 Announce Type: replace Abstract: Facial attribute classification relies on large-scale annotated datasets in which many traits, such as age and expression, are inherently ambiguous...
arXiv:2606. 25769v1 Announce Type: new Abstract: In many prediction problems in medical applications, target labels exhibit an inherent ordinal structure, where class ordering reflects clinically meaningful severity levels.
The paper introduces the Differentiable Fuzzy Inference Layer (DFIL), a dual‑path prediction head that pairs a standard classifier with a scalar‑bottlenecked branch using ordered membership functions. DFIL enforces monotonicity in the underlying quantity and enables compositional reasoning via t‑norm operations, addressing failures of standard classifier heads that treat ordinal categories as independent labels. The scalar branch also offers an interpretable interface for analyzing residual errors, and the authors demonstrate DFIL’s effectiveness on ordinal natural‑language tasks across various large language model families.
The paper introduces MORE-PLR, a method that tackles the partial label ranking problem by employing multi-output regression. It uses an encoder to transform incomplete rankings with ties into regression targets during training, and applies post‑hoc layers during inference to convert regression outputs into bucket orders. Experiments show that this framework competes with state‑of‑the‑art partial label ranking methods.