arXiv:2602.06797v3 Announce Type: replace-cross
Abstract: We study optimal learning rate (LR) schedules under the functional scaling law (FSL) framework (Li et al., 2025), which decomposes training d...
By Binghui Li, Zilin Wang, Fengling Chen, Shiyang Zhao, Ruiheng Zheng, Lei Wu
The paper investigates whether a looped estimator—one that repeatedly applies a single fitted operator with shared parameters—can enhance statistical accuracy while staying within a fixed parameter budget. It establishes upper and lower bounds on squared Hellinger risk for looped sieve maximum likelihood and compares them to the untied counterpart, revealing a tradeoff between parameter sharing, iteration count, and accuracy. For models with known H"older smoothness, looped residual feedforward networks and a post‑layer‑normalized Transformer achieve minimax polynomial rates with a fixed number of bounded real parameters, and under certain conditions the looped estimator’s worst‑case risk vanishes as sample size grows, outperforming the untied approach.
By Xinyu Tian, Xiaotong Shen
arXiv:2512. 22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computational resources.
By Chiwun Yang
arXiv:2303. 08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood.
By Diego Marcondes, Cl\'audia Peixoto
arXiv:2607. 22268v1 Announce Type: cross Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance.
By Hao Yan, Ali Sarabi, Qing Zou, Boyang Xu
arXiv:2606. 27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace.
By Haitong Liu, Deepak Narayanan Sridharan, David Steurer, Manuel Wiedmer