A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics. - Xin Bing; Florentina Bunea; Marten H. Wegkamp (CoRR, 2018)

Prediction in latent factor regression: Adaptive PCR and beyond. - Xin Bing; Florentina Bunea; Seth Strimas-Mackey; Marten H. Wegkamp (CoRR, 2020)

Optimal Estimation of Sparse Topic Models. - Xin Bing; Florentina Bunea; Marten H. Wegkamp (J. Mach. Learn. Res., 2020)

Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond. - Xin Bing; Florentina Bunea; Seth Strimas-Mackey; Marten H. Wegkamp (J. Mach. Learn. Res., 2021)

Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures. - Xin Bing; Marten H. Wegkamp (CoRR, 2022)

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets. - Xin Bing; Tyler Lovelace; Florentina Bunea; Marten H. Wegkamp; Sudhir Pai Kasturi; Harinder Singh; Panayiotis V. Benos; Jishnu Das (Patterns, 2022)

Optimal vintage factor analysis with deflation varimax. - Xin Bing; Dian Jin 005; Yuqian Zhang (CoRR, 2023)

Unique Sparse Decomposition of Low Rank Matrices. - Dian Jin 005; Xin Bing; Yuqian Zhang (IEEE Trans. Inf. Theory, 2023)

Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features. - Chao Wang; Xin Bing; Xin He; Caixing Wang (ICML, 2024)

Learning large softmax mixtures with warm start EM. - Xin Bing; Florentina Bunea; Jonathan Niles-Weed; Marten H. Wegkamp (CoRR, 2024)

One-shot Robust Federated Learning of Independent Component Analysis. - Dian Jin; Xin Bing; Yuqian Zhang (CoRR, 2025)