Team: S. Foucart, F. Narcowich, R. Tuo (lead)

Synopsizing points

• Sampling inequalities and optimal recovery in reproducing kernel Hilbert spaces

• Sparse representation of covariance matrices

• Approximation theory for Gaussian process models with non-Gaussian distributed observations

Recent relevant papers

• R. Tuo, S. He, A. Pourhabib, Y. Ding, J. Z. Huang. A reproducing kernel Hilbert space approach to functional calibration of computer models. Journal of the American Statistical Association, to appear. (doi)

• R. Tuo, W. Wang. Uncertainty quantification for Bayesian optimization. International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, 151, 2862-2884, 2022. (link)

• A. Amir, D. Levin, F. J. Narcowich, J. D. Ward. Meshfree extrapolation with application to enhanced near-boundary approximation with local Lagrange kernels. Foundations of Computational Mathematics 22, 1–34, 2022. (doi)