The paper addresses the bias introduced by two‑level softmax (2LS) sampling, which partitions items into clusters and samples a cluster before an item. It identifies that 2LS misweights clusters by ignoring size imbalance and intra‑cluster similarity dispersion, leading to systematic sampling bias. The authors propose two corrected methods, Size‑Corrected 2LS (S‑2LS) and Size‑ and Dispersion‑Corrected 2LS (SD‑2LS), that eliminate these biases with negligible computational overhead and demonstrate improved sampling performance on five large‑scale datasets.
arXiv:2607. 22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks.
arXiv:2608. 00346v1 Announce Type: new Abstract: Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy.
Fed-BRDECS is a federated deep embedded clustering framework that protects client privacy by replacing globally normalized clustering objectives with a locally computable sample‑stability loss, eliminating the need to share sensitive soft‑assignment distributions. It addresses non‑IID client data through prediction‑balanced sampling, which oversamples rare predicted clusters, and centroid‑level restarting, which refreshes biased or inactive centroids. Experiments on image, text, and time‑series datasets demonstrate that Fed‑BRDECS outperforms existing federated and deep clustering methods while adding no inference‑time cost for anomaly detection.
arXiv:2607.22258v2 Announce Type: replace Abstract: Models trained on long-tailed data using standard softmax tend to exhibit higher training error and a larger generalisation gap for classes with fe...
arXiv:2512.00763v2 Announce Type: replace-cross Abstract: Adaptive and non-Euclidean optimizers often outperform Euclidean methods such as stochastic gradient descent (SGD) in language modeling by a...
arXiv:2606. 27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization.
The paper introduces CBE, a complexity‑adaptive boundary‑aware ensemble method designed to handle class imbalance in classification tasks. It links sampling weights to data complexity, dynamically adjusting boundary preference during under‑sampling, and employs a multi‑level complexity measure to categorize datasets into Easy, Median, and Hard. Experiments on 24 UCI and KEEL datasets show that CBE improves F‑measure and G‑mean on Easy and Median cases without harming performance on Hard cases.
arXiv:2403.14830v2 Announce Type: replace Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensio...
arXiv:2509. 25289v4 Announce Type: replace-cross Abstract: Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue.
The paper introduces the concept of internal discrepancy, measuring the gap between the mean representation of positive samples and latent cluster centers in contrastive learning. It derives a generalization bound for downstream supervised tasks that depends on this discrepancy, providing a unifying framework for existing assumptions and motivating a relaxed k‑means loss to reduce it. The study also explores how contrastive learning affects downstream behavior, particularly in class‑imbalanced settings, and establishes properties of the resulting supervised losses.
arXiv:2606. 25770v1 Announce Type: new Abstract: Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities.
arXiv:2606. 05927v1 Announce Type: new Abstract: The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent labels.