arXiv:2510. 19248v2 Announce Type: replace Abstract: Clustering-based features are widely used in machine learning, but most methods must choose a resolution -- a choice that is global, fixed, and ad hoc.
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The paper introduces OptIDC, a unified framework for optimizing input-side dimensional configuration in deep click‑through rate (CTR) prediction models. OptIDC jointly learns sample, feature, and embedding‑unit selections and employs a selection‑weighted Optimal Transport mechanism to coordinate decisions across levels, reducing conflicts while preserving semantics. Experiments on four real‑world datasets show that OptIDC consistently outperforms existing input‑side selection methods in terms of AUC.
The paper introduces Local Adaptive Probability Estimation (LAPE), a post‑hoc residual calibration framework that refines global calibrator outputs using non‑parametric local evidence from neighboring samples in feature space. LAPE serves as a plug‑and‑play module that leverages feature‑space geometry and reuses calibration data, improving calibration quality for various baselines. The authors also extend the approach to multi‑view inputs with Cascade Feature Augmentation Fusion (CFAF) and demonstrate its effectiveness through extensive experiments.
arXiv:2607. 11052v1 Announce Type: new Abstract: Machine learning progress is often attributed to scaling model size and dataset volume, yet the composition of data can be just as consequential.
arXiv:2608. 10804v1 Announce Type: cross Abstract: Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts.
arXiv:2603. 24025v2 Announce Type: replace Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures.
arXiv:2610.17314v1 Announce Type: cross Abstract: Deep learning has become the dominant form of machine learning due in part, to its ability to automatically engineer complex meta features from data....