Yiming Xu

I am a Postdoctoral Fellow in the Department of Statistics and Actuarial Science at University of Waterloo, mentored by Aukosh Jagannath. Before that, I was a QAP Fellow at Wells Fargo. I obtained my Ph.D. in Mathematics from University of Utah in 2022. My advisors are Akil Narayan and Tom Alberts. More information about me can be found in my CV.

My research is in the area of mathematical data. I have worked on network data, randomized numerical approximation, multifidelity methods, and statistical learning. As a problem-driven researcher, I enjoy learning new ideas from all walks of life and finding connections between them.

Publications

Preprints

• Finding planted cliques using Markov chain Monte Carlo
  Reza Gheissari, Aukosh Jagannath, Yiming Xu
  arXiv (2023)
• A unified analysis of likelihood-based estimators in the Plackett-Luce model
  Ruijian Han, Yiming Xu
  arXiv (2023)
• An approximate control variates approach to multifidelity distribution estimation
  Ruijian Han, Boris Kramer, Dongjin Lee, Akil Narayan, Yiming Xu
  arXiv (2023)
• Wasserstein archetypal analysis
  Katy Craig, Braxton Osting, Dong Wang, Yiming Xu
  arXiv (2022)
• Fast algorithms for monotone lower subsets of Kronecker least squares problems
  Osman A. Malik, Yiming Xu, Nuojin Cheng, Stephen Becker, Alireza Doostan, Akil Narayan
  arXiv (2022)

Journal Publications

• Subsampling of parametric models with bi-fidelity boosting
  Nuojin Cheng, Osman A. Malik, Yiming Xu, Stephen Becker, Alireza Doostan, Akil Narayan
  SIAM-ASA J. Uncertain. Quantif. (to appear)
• Budget-limited distribution learning in multifidelity problems
  Yiming Xu, Akil Narayan
  Numer. Math. (2023)
• Randomized weakly admissible meshes
  Yiming Xu, Akil Narayan
  J. Approx. Theory (2023)
• A general pairwise comparison model for extremely sparse networks
  Ruijian Han, Yiming Xu, Kani Chen
  J. Amer. Statist. Assoc. (2022)
• Probabilistic methods for approximate archetypal analysis
  Ruijian Han, Braxton Osting, Dong Wang, Yiming Xu
  Inf. Inference (2022)
• A bandit-learning approach to multifidelity approximation
  Yiming Xu, Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
  SIAM J. Sci. Comput. (2022)
• Analysis of the ratio of l1 and l2 norms in compressed sensing
  Yiming Xu, Akil Narayan, Hoang Tran, Clayton G. Webster
  Appl. Comput. Harmon. Anal. (2021)
• Consistency of archetypal analysis
  Braxton Osting, Dong Wang, Yiming Xu, Dominique Zosso
  SIMODS (2021)

Conference Proceedings

• Meta learning of interface conditions for multi-domain physics-informed neural networks
  Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Robert M. Kirby, Shandian Zhe
  ICML (2023)
• Nonparametric embeddings of sparse high-order interaction events
  Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe
  ICML (2022)