Analysis of The Ratio of $ ell_1$ and $ ell_2$ Norms in Compressed Sensing. - Yiming Xu 009; Akil Narayan 001; Hoang Tran; Clayton G. Webster (CoRR, 2020)

Probabilistic methods for approximate archetypal analysis. - Ruijian Han; Braxton Osting; Dong Wang 008; Yiming Xu 009 (CoRR, 2021)

Consistency of Archetypal Analysis. - Braxton Osting; Dong Wang 008; Yiming Xu 009; Dominique Zosso (SIAM J. Math. Data Sci., 2021)

Nonparametric Embeddings of Sparse High-Order Interaction Events. - Zheng Wang 042; Yiming Xu 009; Conor Tillinghast; Shibo Li; Akil Narayan 001; Shandian Zhe (ICML, 2022)

Fast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems. - Osman Asif Malik; Yiming Xu 009; Nuojin Cheng; Stephen Becker; Alireza Doostan; Akil Narayan 001 (CoRR, 2022)

Quadrature Sampling of Parametric Models with Bi-fidelity Boosting. - Nuojin Cheng; Osman Asif Malik; Yiming Xu 009; Stephen Becker; Alireza Doostan; Akil Narayan 001 (CoRR, 2022)

A Bandit-Learning Approach to Multifidelity Approximation. - Yiming Xu 009; Vahid Keshavarzzadeh; Robert M. Kirby; Akil Narayan 001 (SIAM J. Sci. Comput., 2022)

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. - Shibo Li; Michael Penwarden; Yiming Xu 009; Conor Tillinghast; Akil Narayan 001; Mike Kirby; Shandian Zhe (ICML, 2023)

An approximate control variates approach to multifidelity distribution estimation. - Ruijian Han; Akil Narayan 001; Yiming Xu 009 (CoRR, 2023)

Budget-limited distribution learning in multifidelity problems. - Yiming Xu 009; Akil Narayan 001 (Numerische Mathematik, 2023)

Randomized weakly admissible meshes. - Yiming Xu 009; Akil Narayan 001 (J. Approx. Theory, 2023)

Subsampling of Parametric Models with Bifidelity Boosting. - Nuojin Cheng; Osman Asif Malik; Yiming Xu 009; Stephen Becker; Alireza Doostan; Akil Narayan 001 (SIAM/ASA J. Uncertain. Quantification, 2024)

An Approximate Control Variates Approach to Multifidelity Distribution Estimation. - Ruijian Han; Boris Kramer; Dongjin Lee; Akil Narayan 001; Yiming Xu 009 (SIAM/ASA J. Uncertain. Quantification, 2024)

Hybrid least squares for learning functions from highly noisy data. - Ben Adcock; Bernhard Hientzsch; Akil Narayan 001; Yiming Xu 009 (CoRR, 2025)