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:
- Xin Bing and Marten Wegkamp.
Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures.
Biometrika, 111(1): 291-308. March 2024. [Paper].
- 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), Sep 2023. [Paper].
- 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].
- 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].
- 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].
- 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].
- 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].
- 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].
- 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].
- Xin Bing, Florentina Bunea and Marten Wegkamp.
Optimal estimation of sparse topic models.
Journal of Machine Learning Research, 21(177): 1−45, 2020. [Paper].
- 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].
- 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].
- 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, Feb 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.