arXiv:2604. 01506v2 Announce Type: replace Abstract: Long-tailed classification, where a small number of frequent classes dominate many rare ones, remains challenging because models systematically favor frequent classes at inference time.
By Zhanliang Wang, Hongzhuo Chen, Quan Minh Nguyen, Mian Umair Ahsan, Kai Wang
arXiv:2605. 05189v2 Announce Type: replace-cross Abstract: How many key-value associations can a $d\times d$ linear memory store?
By Nicholas Barnfield, Juno Kim, Eshaan Nichani, Jason D. Lee, Yue M. Lu
The paper demonstrates that common binary classification metrics—Matthews' correlation coefficient, Cohen's κ, the F-score, and the Jaccard similarity—are not robust to extreme class imbalance, as the Bayes classifier’s true positive rate tends to zero when the minority class proportion vanishes. To address this, the authors propose robustified versions of these metrics that include a tuning parameter, ensuring that the Bayes-optimal classifier’s threshold remains bounded and its true positive rate stays above zero even in highly imbalanced scenarios. The study provides theoretical bounds, simulation results, and practical guidance on applying these robust metrics to real data, such as a credit‑default dataset, and discusses their relationship to ROC and precision‑recall curves.
By Hajo Holzmann, Bernhard Klar
The paper introduces the Adaptive Margin Ordinal Loss (AMOL), a new loss function designed to reduce the tendency of neural networks to predict center classes in ordinal classification tasks—a problem called center‑class hedging. AMOL applies a multiplicative weight to per‑class loss terms that is large only when a candidate class is near the center while the true label is far from it, thereby discouraging hedging. The authors also propose the Center‑Hedging Rate (CHR) metric to quantify this failure mode and demonstrate that AMOL achieves state‑of‑the‑art Quadratic Weighted Kappa scores on four benchmarks, with an asymmetric variant eliminating hedging on the Abalone dataset.
By Manisha Kandel
The paper introduces Rank‑Variance Scores (TRV) as a new metric for importance sampling and data pruning in the presence of label contamination. It shows that traditional loss‑based methods fail because corrupted examples often have high loss, and proves that a useful score must be non‑monotonic in loss. By ranking examples across multiple independently trained proxy models, TRV captures both between‑proxy disagreement and within‑proxy variance, enabling Disagreement‑Regularized Importance Sampling (DR‑IS) and TRV‑based pruning to outperform random selection on noisy CIFAR‑10/100 datasets.
By Csongor Horv\'ath, Ida-Maria Sintorn, Prashant Singh
arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.
By Rostislav Gusev, Alexey Zaytsev