The paper introduces Selective Posterior Margin Regularization (SPMR), a technique that enhances Forward correction for learning with class‑conditional label noise. SPMR preserves the Forward objective while converting disagreements between the corrected likelihood’s reverse posterior and the observed annotation into a graded update on the clean classifier. Experiments on five known‑transition benchmarks show that SPMR improves performance by 2.5–7.0 percentage points over full‑length Forward and remains 0.7–2.5 percentage points better when combined with Mixup and early stopping, with gains attributed to posterior‑space coefficients, transition‑adjusted targets, and pairwise actions.
By Zexing Zhang, Jichao Li, Tianyang Lei, XiongYi Lu, Yang Kewei
arXiv:2608. 06896v1 Announce Type: new Abstract: Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data.
By Wei Wang, Gang Niu, Masashi Sugiyama
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.
By Jinshi Liu, Lei He, Pan Liu
arXiv:2512.12870v2 Announce Type: replace-cross
Abstract: Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are of...
By Pouya Ahadi, Blair Winograd, Camille Zaug, Karunesh Arora, Lijun Wang, Kamran Paynabar
Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data. However, this requirement is often not met in real-world applications.
arXiv:2605. 20721v2 Announce Type: replace Abstract: Label noise is a central challenge in learning from implicit feedback for recommendation.
By Zongyu Li, Xuanyu Liu, Gongce Cao, Shirui Sun, Yaqi Fang, Yongshuai Yu
arXiv:2606. 00512v1 Announce Type: new Abstract: In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce.
By Kwangho Kim, Jisu Kim
The paper introduces soft‑label‑based estimators for the Bayes‑optimal balanced error rate (BER) and area under the ROC curve (AUC), extending from a clean setting with known class priors to a realistic scenario with unknown priors and corrupted soft labels. It also adapts the FeeBee evaluation framework to assess these estimators without needing the true optimum, providing practical evaluation scores for any estimator of optimal BER or AUC. Experiments on synthetic and real datasets confirm the effectiveness of both the estimators and the evaluation method.
By Ryota Ushio, Takashi Ishida, Masashi Sugiyama
arXiv:2601. 15036v4 Announce Type: replace Abstract: Factorizable joint shift (FJS) represents a type of distribution shift (or dataset shift) that comprises both covariate and label shift.
By Dirk Tasche
PaSta introduces a Partial label-based Self‑training framework for noisy node classification on graphs. The method trains multiple annotators to generate high‑quality partial labels, then uses a partial‑label classification model with two loss functions to learn both labels and representations. A closed‑loop self‑training strategy further refines annotators, yielding an average 1.1% improvement over state‑of‑the‑art methods across five datasets.
By Yujing Liu, Yixin Liu, Yu Zheng, Yue Tan, Alan Wee-Chung Liew, Shirui Pan
arXiv:2407. 05370v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data.
By Zeju Li, Ying-Qiu Zheng, Chen Chen, Saad Jbabdi
arXiv:2609.14802v1 Announce Type: cross
Abstract: Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty assoc...
By Mushan Li, Kihyun Han, Yanyuan Ma