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Refereed Papers

Most of these are available on arXiv. Corresponding software is available at the bottom of this page.

For a broad description of current interests, see my research page.

Preprints

(* denotes equal contribution)

• Li, A., Tokdar, S. and Xu, J. A Bayesian decision-theoretic approach to sparse estimation.

• Suder, P., Xu, J and Dunson, D. Bayesian Transfer Learning. Minor revision. Link

• Morsomme, R. and Xu, J. Exact Inference for Stochastic Epidemic Models via Uniformly Ergodic Block Sampling. Minor revision (ASA Statistics in Epidemiology Young Investigator Paper Award). Link

• Morsomme, R., Halloway, S., Ryser, M.* and Xu, J*. A Bayesian approach for fitting semi-Markov mixture models of cancer latency to individual-level screens. Link

• Zheng, C., Dilma, E., Xu, J. and Duan, L. The Bridged Posterior: Optimization, Profile Likelihood and a New Approach to Generalized Bayes. Link

• Morsomme, R.*, Chen, M.*, and Xu, J. A family of flexible and scalable temporal stochastic epidemic models.

• He, M., Lu, D., Xu, J.* and Xavier, R*. Community Detection in Weighted Multilayer Networks with Ambient Noise. Link

• Gustafson, A., Hirn, M., Mohammed, K., Narayanan, H. and Xu, J. Structural Risk Minimization for C^1,1(R^d) Regression. Link

Thesis

• Xu, J. (2016). Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes. PhD Dissertation, Department of Statistics, University of Washington. Link

Publications

(* denotes equal contribution)

• Bu, F., Aiello, A., Volfovsky, A.* and Xu, J.* (2025). Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity. Biostatistics. Link

• Chandra, N., Dunson, D. and Xu, J. (2024). Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis. Journal of the American Statistical Association. Link

• Jiang, H. and Xu, J. The Stochastic Proximal Distance Algorithm. (ASA Statistical Computing and Statistical Graphics Section Student Paper Competition Award). Statistics and Computing. Link

• Agarwal, M. and Xu, J. (2024). Quasi-Newton Acceleration of EM and MM Algorithms via Broyden’s Method with Extrapolation. Journal of Computational and Graphical Statistics. Link

• Huang, J., Morsomme, R., Dunson, D. and Xu, J. (2024). Detecting Changes in the Transmission Rate of a Stochastic Epidemic Model. Statistics in Medicine. Link

• Bu, F., Galiwango, R., Grabowski, K., Ratmann, O. and Xu, J. (2024). Inferring HIV Transmission Patterns from Viral Deep-Sequence Data via Latent Typed Point Processes. Biometrics. Link

• Awasthi, A., Minin, V., Huang, J., Chow, D. and Xu, J. (2023). Fitting a Stochastic Model of Intensive Care Occupancy to Noisy Hospitalization Time Series. Statistics in Medicine. Link

• Landeros, A., Xu, J. and Lange, K. (2023). MM Optimization Algorithms: Proximal Distance Algorithms, Path Following, and Trust Regions. Proceedings of the National Academy of Sciences (PNAS). Link

• Presman, R. and Xu, J. (2023). Distance-to-Set Priors and Constrained Bayesian Inference. Artificial Intelligence and Statistics. (Oral) Link

• Awasthi, A. and Xu, J. (2023). Fast Computation of Branching Process Transition Probabilities via ADMM. Artificial Intelligence and Statistics. Link

• Won, J., Lange, K. and Xu, J. (2023). A Unified Analysis of Convex and Non-convex l_p-ball Projection Problems. Optimization Letters. Link

• Paul, D.*, Chakraborty, S.*, Das, S. and Xu, J. (2023). Implicit Annealing in Kernel and Multi-kernel Spaces: A Strongly Consistent Clustering Approach. IEEE Transactions in Pattern Analysis and Machine Intelligence. Link

• Chakraborty, S. and Xu, J. (2023). Biconvex Clustering. Journal of Computational and Graphical Statistics. Link

• Xu, J. and Lange, K. (2022). A Proximal Distance Algorithm for Likelihood-based Sparse Covariance Estimation. Biometrika. Link

• Scalabrino, M.L, Thapa , M., Chew, L. A., Zhang, E., Xu, J., Sampath, A. P., Chen, J., Field, G.D. (2022). Robust Cone-mediated Signaling Persists Late into Rod Photoreceptor Degeneration. eLife. Link

• Vellal, A., Chakraborty, S. and Xu, J. (2022). Bregman Power k-Means for Clustering Exponential Family Data. International Conference on Machine Learning. Link

• Bu, F., Aiello, A., Xu, J.* and Volfovsky, A* (2022). Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks. Journal of the American Statistical Association. (SBSS Student Paper Award) Link

• Stutz, T., Sehl, M., Sinsheimer, J. and Xu, J (2022). Computational Tools for Assessing Gene Therapy under Branching Process Models of Mutation. Bulletin of Mathematical Biology. Link

• Paul, D.*, Chakraborty, S.*, Das, S. and Xu, J (2021). Uniform Concentration Bounds toward a Unified Framework for Robust Clustering. Neural Information Processing Systems (NeurIPS). (Spotlight Paper) Link

• Landeros, A., Ji, X., Lange, K. L., Stutz, T., Xu, J., Sehl, M. E.*, and Sinsheimer, J.S* (2021). An Examination of School Reopening Strategies during the SARS-CoV-2 Pandemic. PLOS One. Link

• Ciccone, E.J., et al. (2021). SARS-CoV-2 Infection in Healthcare Personnel and Their Household Contacts at a Tertiary Academic Medical Center: Protocol for a Longitudinal Cohort Study. JMIR Research Protocols. Link

• Stutz, T., Landeros, A., Xu, J., Sinsheimer, J. S., Lange, K. and Sehl, M. E (2021). Stochastic Simulation Algorithms for Interacting Particle Systems. PLOS One. Link

• Zhang, Z., Lange, K. and Xu, J. (2020). Simple and Scalable Sparse k-Means Clustering via Feature Ranking. Neural Information Processing Systems (NeurIPS). (Spotlight Paper) Link

• Chakraborty, S.*, Paul, D.*, Das, S. and Xu, J. (2020). Entropy Weighted Power k-Means Clustering. Artificial Intelligence and Statistics (AISTATS). Link

• Xu, J., Koelle, S., Wu, C., Guttorp, P., Dunbar, C. E., Abkowitz, J. L. and Minin, V. N. (2019). Statistical Inference for Partially Observed Branching Processes, with Application to Cell Lineage Tracking of in vivo Hematopoiesis. The Annals of Applied Statistics. Link

• Xu, J. and Lange, K (2019). Power k-Means Clustering. International Conference on Machine Learning (ICML). (Long Oral) Link

• Won, J., Xu, J and Lange, K. (2019). Projection onto Minkowski Sums with Application to Constrained Learning. International Conference on Machine Learning (ICML). (Long Oral) Link

• Xu, J., Wang, Y., Guttorp, P. and Abkowitz, J. L. (2018). Visualizing Hematopoiesis as a Stochastic Process. Blood. Link

• Ho, L., Xu, J., Crawford, F. W., Minin, V. N. and Suchard, M. A. (2018). Birth/birth-death Processes and their Computable Transition Probabilities with Biological Applications. Journal of Mathematical Biology. Link

• Xu, J., Chi, E., Yang, M. and Lange, K. (2018). A Majorization-minimization Algorithm for Split Feasibility Problems. Computational Optimization and Applications. Link

• Xu, J., Chi, E. and Lange, K. (2018). Generalized Linear Model Regression under Distance-to-set Penalties. Neural Information Processing Systems (NeurIPS). (Spotlight Paper) Link

• Letcher, A.*, Trišović, J.*, Cademartori, C., Chen, X. and Xu, J. (2018). Automatic Conflict Detection in Police Body-Worn Audio. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (MAA Outstanding Student Research Award) Link

• Hardin, W., Li, R., Xu, J., Shelton, A., Alas G., Minin, V. N. and Paredez, A. R. (2017). Myosin-independent cytokinesis in Giardia Utilizes Flagella to Coordinate Force Generation and Direct Membrane Trafficking. Proceedings of the National Academy of Sciences (PNAS). Link

• Koelle, S., Wu, C., Xu, J., Lu, R., Li, B., Donahue, R. E. and Dunbar, C. E. (2017). Quantitative Stability of Hematopoietic Stem and Progenitor Cell Clonal Output in Rhesus Macaques Receiving Transplants. Blood. Link

• Xu, J., Koelle, S., Wu, C., Guttorp, P., Dunbar, C. E., Abkowitz, J. L. and Minin, V. N. (2016). Stochastic Modeling of Hematopoietic Stem and Progenitor Cell Barcoding Data from Rhesus Macaques Challenges the Classic Model of Hematopoiesis. Blood. Link

• Xu, J., Guttorp, P., Kato-Maeda, M. and Minin, V. N. (2015). Likelihood-based Inference for Discretely Observed Birth-death-shift Processes, with Applications to Evolution of Mobile Genetic Elements. Biometrics. (David P. Byar Paper Competition Award) Link

• Xu, J. and Minin, V. N. (2015). Efficient Transition Probability Computation for Continuous-time Branching Processes via Compressed Sensing. Uncertainty in Artificial Intelligence (UAI). Link

• Foti, N.*, Xu, J.*, Laird, D. and Fox, E. B. (2015). Stochastic Variational Inference for Hidden Markov Models. Neural Information Processing Systems (NeurIPS). Link

• Huber, M. L., McCall, E., Rozenfeld, D. and Xu, J. (2012). Bounds on the Artificial Phase Transition for Perfect Simulation of Repulsive Point Processes. Involve. Link

Software

• SUFA: R package for identifiable subspace factor analysis, with fast parallelized HMC-within-Gibbs sampler

• HIVflow: R and Python code for learning transmission flow in latent spatial point process model of transmission structure via data-augmented MCMC

• BCC: R code, biconvex clustering with adaptive feature selection

• PDSIR: R package, fast data-augmented MCMC for sampling from the exact stochastic SIR posterior under incidence data
  • Extension with change-point detection and heterogeneous transmission rate here!

• QNMM: R package for quasi-Newton acceleration of MM and EM algorithms

• SKFR: R and Julia code for sparse k-means with feature ranking

• spcov: Julia implementation of proximal distance algorithm for sparse covariance estimation

• pkmeans: Python implementation of power k-means including Bregman divergences

• CoEpiNet: R code for inference for partially observed epidemics over dynamic contact networks

• EWP: R code for power k-means clustering with entropy-based feature weighing

• MinkowskiProjection: Matlab code for projection onto Minkowski sums of sets

• splitFeas: R package, MM algorithms for multi-set split feasibility problems

• branchCorr: R package, M-estimation for partially observed stochastic compartmental models

• bdsem: R package, MLE and EM inference for discretely observed multi-type branching processes

• pysvihmm: Python implementation of stochastic variational inference for hidden Markov models

• multiBD: R package, likelihood inference for partially observed multivariate birth-death processes

• hematopoiesisSimulator: Java GUI for simulating and visualizing stochastic models of hematopoiesis

Please email me for any code not yet released in a completed package.

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