6 citations · 13 across the 11 of their papers we have counts for
12 papers
Balancing fractional Brownian motion
Hengrui Luo, Yiming Xu
We study the discrepancy of balancing independent sample paths of fractional Brownian motion with Hurst exponent on , an infinite-dimensional analogue of bal…
Hybrid least squares for learning functions from highly noisy data
Ben Adcock, Bernhard Hientzsch, Akil Narayan +1
Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with heavily polluted data. Existing methods…
Optimally balancing exploration and exploitation to automate multi-fidelity statistical estimation
Thomas Dixon, Alex Gorodetsky, John Jakeman +2
Multi-fidelity methods that use an ensemble of models to compute a Monte Carlo estimator of the expectation of a high-fidelity model can significantly reduce computational costs co…
An approximate control variates approach to multifidelity distribution estimation
Ruijian Han, Boris Kramer, Dongjin Lee +2
Forward simulation-based uncertainty quantification that studies the distribution of quantities of interest (QoI) is a crucial component for computationally robust engineering desi…
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Shibo Li, Michael Penwarden, Yiming Xu +4
Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply differen…
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting
Nuojin Cheng, Osman Asif Malik, Yiming Xu +3
Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space explorat…
Fast algorithms for least square problems with Kronecker lower subsets
Osman Asif Malik, Yiming Xu, Nuojin Cheng +3
While leverage score sampling provides powerful tools for approximating solutions to large least squares problems, the cost of computing exact scores and sampling often prohibits p…
Nonparametric Embeddings of Sparse High-Order Interaction Events
Zheng Wang, Yiming Xu, Conor Tillinghast +3
High-order interaction events are common in real-world applications. Learning embeddings that encode the complex relationships of the participants from these events is of great imp…
Budget-limited distribution learning in multifidelity problems
Yiming Xu, Akil Narayan
Multifidelity methods are widely used for estimating quantities of interest (QoI) in computational science by employing numerical simulations of differing costs and accuracies. Man…
A bandit-learning approach to multifidelity approximation
Yiming Xu, Vahid Keshavarzzadeh, Robert M. Kirby +1
Multifidelity approximation is an important technique in scientific computation and simulation. In this paper, we introduce a bandit-learning approach for leveraging data of varyin…
Randomized weakly admissible meshes
Yiming Xu, Akil Narayan
A weakly admissible mesh (WAM) on a continuum real-valued domain is a sequence of discrete grids such that the discrete maximum norm of polynomials on the grid is comparable to the…
Analysis of The Ratio of and Norms in Compressed Sensing
Yiming Xu, Akil Narayan, Hoang Tran +1
We first propose a novel criterion that guarantees that an -sparse signal is the local minimizer of the objective; our criterion is interpretable and useful in p…