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.
By Chaohua Yang, Fuyuan Lyu, Xing Tang, Dugang Liu, Xiuqiang He, Zhong Ming
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.
By Xihao Wang, Chengyao Yu, Ruixing Ming
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.
By Kimia Hamidieh, Lester Mackey, David Alvarez-Melis
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.
By Qiang Wang, Songlin Dong, Shaokun Wang, Jizhou Han, Xiang Song, Chenhao Ding, Yuhang He, Yihong Gong
arXiv:2603. 24025v2 Announce Type: replace Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures.
By Chen Ma, Wanjie Wang, Shuhao Fan
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....
By Rishav Mukherjee, Jeffrey Ahearn Thompson
arXiv:2610.07562v1 Announce Type: cross
Abstract: Learning an ensemble of GFlowNets to sample from a discrete target distribution has become a common approach for achieving better state space explora...
By Tiago da Silva, Amauri H. Souza, Salem Lahlou
arXiv:2606. 25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training.
By Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen, Kok-Seng Wong
arXiv:2610.00873v1 Announce Type: cross
Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between...
By Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan, Kunpeng Liu, Haifeng Chen, Yanjie Fu
arXiv:2603.18773v2 Announce Type: replace-cross
Abstract: LLM post-training pipelines that combine supervised fine-tuning and reinforcement learning are difficult to configure under realistic compute...
By Channe Chwa, Xinle Wu, Yao Lu
IntactWorld introduces a Joint World Modeling Architecture that preserves uncompressed Intact Features to better capture real‑world logic in video generation. By predicting clean intermediate features instead of flow velocity in a high‑dimensional space, it avoids manifold gaps and reduces computational overhead through a Full‑to‑Compact Training Paradigm that replaces raw features with refined CLS tokens. The approach cuts spatial memory usage by 11.4% and inference latency by 43.8%, achieving a 2.46‑point improvement on the VBench 2.0 benchmark.
By Boming Tan, Xiangdong Zhang, Yan Xia, Qi Zhu, Deyi Ji, Xue Yang, Shaofeng Zhang
Deep click-through rate (CTR) prediction has achieved substantial success through increasingly expressive feature interaction architectures. However, predictive performance also depends on how input-s...