Publications

Papers under review:

  • Xin Bing, Bingqing Li and Marten Wegkamp. Linear Discriminant Regularized Regression. [arXiv].
  • Xin Bing and Shangkai Zhu. Double denoising k-means clustering.
  • Xin Bing, Dian Jin and Yuqian Zhang. Optimal vintage factor analysis with deflation varimax. [arXiv].
  • Xin Bing, Florentina Bunea and Jonathan Niles-Weed. The Sketched Wasserstein Distance for mixture distributions. [arXiv].
  • Eugen Pircalabelu and Xin Bing. Overlapping clustering of time dependent variables for fMRI data. 

Papers published in journals:

  1. Xin Bing, Wei Cheng, Huijie Feng and Yang Ning.
    Inference in High-dimensional Multivariate Response Regression with Hidden Variables.
     Journal of American Statistical Association (Theory & Method), 2023. [Paper].
  2. Xin Bing and Marten Wegkamp.
    Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures.
     Biometrika, 2023. [Paper].
  3. Xin Bing and Marten Wegkamp.
    Optimal Discriminant Analysis in High-Dimensional Latent Factor Models.
    The Annals of Statistics, 51(3): 1232-1257. June 2023. [Paper].
  4. Dian Jin, Xin Bing and Yuqian Zhang.
    Unique sparse decomposition of low rank matrices.
    IEEE Transactions on Information Theory, 69(4): 2452-2484, April 2023. [Paper].
  5. Xin Bing, Florentina Bunea, Seth Strimas-Mackey and Marten Wegkamp.
    Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations.
    The Annals of Statistics, 50(6): 3307-3333, December 2022. [Paper].
  6. Xin Bing, Florentina Bunea and Marten Wegkamp.
    Detecting approximate replicate components of a high-dimensional random vector with latent structure.
    Bernoulli, 29(2): 1368-1391, 2023. [Paper].
  7. Xin Bing, Florentina Bunea and Marten Wegkamp.
    Inference in latent factor regression with clusterable features.
    Bernoulli, 28(2): 997 – 1020, May 2022. [Paper][R-package].
  8. Xin Bing, Yang Ning and Yaosheng Xu.
    Adaptive estimation of multivariate response regression with hidden variables.
     The Annals of Statistics, 50(2): 640-672, 2022. [Paper].
  9. Xin Bing, Florentina Bunea, Seth Strimas-Mackey and Marten Wegkamp.
    Prediction in latent factor regression: Adaptive PCR and beyond.
     Journal of Machine Learning Research, 22(177):1−50, 2021. [Paper].
  10. Xin Bing, Florentina Bunea and Marten Wegkamp.
    Optimal estimation of sparse topic models.
     Journal of Machine Learning Research, 21(177): 1−45, 2020. [Paper].
  11. Xin Bing, Florentina Bunea and Marten Wegkamp.
    A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics.
     Bernoulli, 26(3): 1765 – 1796, August 2020. [Paper].
  12. Xin Bing, Florentina Bunea, Yang Ning and Marten Wegkamp.
    Adaptive estimation in structured factor models with applications to overlapping clustering.
     The Annals of Statistics, 48(4): 2055 – 2081, August 2020. [Paper][R-pacakge].
  13. Xin Bing and Marten Wegkamp.
    Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models.
     The Annals of Statistics, 47(6): 3157 – 3184, December 2019. [Paper][R-package].

Papers published in conferences:

  • Dian Jin, Xin Bing and Yuqian Zhang.
    Unique sparse decomposition of low rank matrices.
    NeurIPS (2021). [arXiv].

Applications:

  • Javad Rahimikollu, Hanxi Xiao, Anna E. Rosengart, Tracy Tabib, Paul Zdinak, Kun He, Xin Bing, Florentina Bunea, Marten Wegkamp, Amanda C. Poholek, Alok V Joglekar, Robert A Lafyatis, Jishnu Das.
    SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.
    Nature methods (2024) [Paper].
  • Xin Bing, Tyler Lovelace, Florentina Bunea, Marten Wegkamp, Harinder Singh, Panayiotis Benos, Jishnu Das.
    Essential Regression – a generalizable framework for inferring causal latent factors from multi-omic human datasets.
    Patterns (Cell press), 3(5): 100473, 2022. [bioRxiv].
  • Xin Bing, Florentina Bunea, Martin Royer and Jishnu Das.
    Latent Model-Based Clustering for Biological Discovery.
     iScience, 14: 125 – 135, 2019.

Discussions:

  • Xin Bing and Marten Wegkamp. Discussion of  Random-projection Ensemble Classification by​ Timothy I. Cannings and Richard J. Samworth. J. R. Statist. Soc. B, 79(4), 1006-1007, 2017.