publications

Papers Under Review

  • Xin Bing and Derek Latremouille. High-Dimensional Invariant Tests of Multivariate Normality Based on Radial Concentration. [arXiv].
  • Xin Bing, Xin He and Chao Wang. Kernel Ridge Regression with Predicted Feature Inputs and Applications to Factor-Based Nonparametric Regression.
  • Xin Bing, Florentina Bunea, Jonathan Niles-Weed and Marten Wegkamp. Learning large softmax mixtures with warm start EM. [arXiv].
  • Xin Bing, Bingqing Li and Marten Wegkamp. Linear Discriminant Regularized Regression. [arXiv].
  • Xin Bing and Shangkai Zhu. Double denoising k-means clustering.
  • Eugen Pircalabelu and Xin Bing. Overlapping clustering of time dependent variables for fMRI data.

Papers Published in Journals

  1. Xin Bing, Xin He, Dian Jin and Yuqian Zhang.
    Optimal vintage factor analysis with deflation varimax.
    The Annals of Statistics, to appear (2025+). [arXiv].
  2. Xin Bing, Florentina Bunea and Jonathan Niles-Weed.
    The Sketched Wasserstein Distance for mixture distributions.
    Bernoulli, to appear (2025+). [arXiv].
  3. Xin Bing and Marten Wegkamp.
    Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures.
    Biometrika, 111(1): 291-308, March 2024. [Paper].
  4. 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), 119(547), 2066–2077. Sep 2023. [Paper].
  5. 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].
  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, May 2023. [Paper].
  7. 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.
  8. 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].
  9. 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].
  10. 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].
  11. 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].
  12. Xin Bing, Florentina Bunea and Marten Wegkamp.
    Optimal estimation of sparse topic models.
    Journal of Machine Learning Research, 21(177): 1−45, 2020. [Paper].
  13. 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].
  14. 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-package].
  15. 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

  1. Chao Wang, Xin Bing, Xin He and Caixing Wang.
    Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features.
    ICML, spotlight, 2024. [Paper].
  2. 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, 21, 835–845 (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. [Paper].
  • 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.