arXiv:2606. 01427v1 Announce Type: cross Abstract: Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning.
arXiv:2608. 13793v1 Announce Type: cross Abstract: Machine learning (ML) has become an indispensable part of modern engineering design workflows.
arXiv:2608.29349v1 Announce Type: new Abstract: Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-e...
arXiv:2606. 17513v1 Announce Type: cross Abstract: Neural operators provide fast surrogates for PDEs but their deterministic predictions limit their use in tasks requiring uncertainty quantification (UQ), especially under geometric variability.
arXiv:2606. 29241v1 Announce Type: new Abstract: Data-generating priors are a central component of tabular foundation models because they define the task distribution used during pretraining.
arXiv:2609.25430v1 Announce Type: new Abstract: Neural surrogates can substantially accelerate computer-aided engineering (CAE) workflows, but their use in design requires uncertainty estimates that...
Xiaomi-TabLDM is a tabular foundation model that performs classification and regression via in-context learning without task‑specific fine‑tuning. It is pretrained solely on synthetic data from structural causal models, achieving top‑ranked regression results on multiple benchmarks while reducing training and prediction time compared to leading models. The architecture incorporates a three‑stage training strategy, dual‑stream feature grouping, lightweight attention residuals, and sparse mixture‑of‑experts, and it can further improve accuracy through test‑time compute scaling.
The paper introduces HACK GPs, a method that treats kernel selection for Gaussian Processes as an online learning problem with expert advice. Each candidate kernel is viewed as a GP expert, and a distribution over these experts is updated online using AdaHedge based on a loss that reflects both function fit and task alignment. Two variants—Mixture of Gaussians and categorical sampling—are presented, with theoretical guarantees that the weight concentrates on the best kernel under a loss‑gap condition, and empirical results show robust performance across Bayesian optimization, level set estimation, and Bayesian active learning compared to standard kernels and simple ensembles.
arXiv:2607. 07863v1 Announce Type: new Abstract: In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm.
arXiv:2602. 22422v2 Announce Type: replace-cross Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis.
arXiv:2510. 25599v2 Announce Type: replace Abstract: Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification-focused.
arXiv:2609.36883v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without...
The paper proposes three information‑theoretic criteria for selecting the most relevant basis functions in sparse Gaussian process regression, tailored to different levels of prior knowledge. Experiments on six UCI regression datasets and three basis families (HSGP, VFF, VISH) show that the no‑data criterion is a robust default, often outperforming simple truncation, while the data‑aware criteria yield significant improvements for HSGP. The study demonstrates that careful basis‑function selection can lead to better performance without increasing computational cost.