3 papers
cs.LG2026
Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings
Lyle Regenwetter, Rosen Yu, Cyril Picard +1
Predictive modeling in engineering applications has long been dominated by bespoke models and small, siloed tabular datasets, limiting the applicability of large-scale learning app…
cs.LG2026
FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models
Rosen Ting-Ying Yu, Nicholas Sung, Faez Ahmed
Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance, where the commonly-used Gaussian-process (GP) surrogates struggle with cubic scaling costs and o…
cs.CE2025
GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models
Rosen Ting-Ying Yu, Cyril Picard, Faez Ahmed
Bayesian optimization (BO) struggles in high dimensions, where Gaussian-process surrogates demand heavy retraining and brittle assumptions, slowing progress on real engineering and…