arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.
By Yi He, Zhengkang Guan, Anpeng Wu, Peng Cui, Fei Wu, Kun Kuang
The paper evaluates TabPFN, a tabular foundation model, for motor insurance pricing by comparing it to generalized linear models (GLMs) and XGBoost on two motor third‑party liability datasets. Results show that TabPFN does not consistently outperform GLMs: it lags on claim frequency, especially when the in‑context training set is small, but performs competitively on claim severity where data are limited. Additionally, TabPFN’s inference times are substantially longer than those of the baseline methods.
By Bruno Deprez, Wouter Verbeke, Tim Verdonck
arXiv:2608. 09162v1 Announce Type: cross Abstract: Tabular data presents unique challenges for deep learning due to its heterogeneous nature, where numeric features exhibit diverse distributions, scales, and statistical properties.
By Zihao Ye, Juyong Kim, Johnna Sundberg, Burak Varici, Pradeep Ravikumar
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
By Minh-Khoi Pham, Luca Cotugno, Alina Sirbu, Tai Tan Mai, Martin Crane, Marija Bezbradica
arXiv:2604. 20288v2 Announce Type: replace Abstract: Flight diversions are rare but high-impact events in aviation, making their reliable prediction vital for both safety and operational efficiency.
By Karim Aly, Alexei Sharpanskykh, Jacco Hoekstra
arXiv:2610.27789v1 Announce Type: new
Abstract: Empirical scaling laws relate validation loss to model size, training exposure and compute, providing a quantitative basis for allocating resources. Th...
By Tianyu Zhao, Zhixiang Ren