69 citations · 189 across the 30 of their papers we have counts for
6 papers · 1 filter
On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization
Tyler R. Johnson, Kian Ben-Jacob, Christopher P. Muller +1
Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which ca…
On the Uncertainty Quantification Ability of Tabular Foundation Models
Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh +2
Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning. However, many critical applications in mecha…
Unsupervised Anomaly Detection via Nonlinear Manifold Learning
Amin Yousefpour, Mehdi Shishehbor, Zahra Zanjani Foumani +1
Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in a…
Multi-Fidelity Cost-Aware Bayesian Optimization
Zahra Zanjani Foumani, Mehdi Shishehbor, Amin Yousefpour +1
Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sol…
Data Fusion with Latent Map Gaussian Processes
Nicholas Oune, Jonathan Tammer Eweis-Labolle, Ramin Bostanabad
Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in engineering design. In this paper, we introduce a novel approach based on latent-map Gaussi…
Latent Map Gaussian Processes for Mixed Variable Metamodeling
Nicholas Oune, Ramin Bostanabad
Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. In this paper,…