Joshua Agterberg

Research

Research Interests

  • Statistical Network Analysis
  • High-dimensional Statistics
  • Spectral Methods
  • Mathematical Data Science
  • Nonparametric Statistics
  • Nonconvex Optimization

Prospective PhD students, please read this.

Click here for representative papers

Preprints

  • Statistical Inference for Low-Rank Tensors: Heteroskedasticity, Subgaussianity, and Applications
    Joshua Agterberg and Anru Zhang, 2024.
    [arXiv].

  • A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks
    Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg, and René Vidal, 2024.
    [arXiv].

  • ICL-TSVD: Bridging Theory and Practice in Continual Learning with Pre-Trained Models
    Liangzu Peng, Juan Elenter, Joshua Agterberg, Alejandro Ribeiro, and René Vidal, 2024.
    [arXiv].

  • Distributional Theory and Statistical Inference for Linear Functions of Eigenvectors with Small Eigengaps
    Joshua Agterberg, 2023.
    [arXiv].

  • Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
    Joshua Agterberg, Zachary Lubberts, and Jesús Arroyo, 2023.
    [arXiv].

  • Nonparametric Two-Sample Hypothesis Testing for Random Graphs with Negative and Repeated Eigenvalues
    Joshua Agterberg, Minh Tang, and Carey Priebe, Submitted, 2020.
    (Selected as a finalist for the Nonparametric Statistics Student Competition, JSM 2021.)
    (Won best presentation award for the Nonparametric Statistics Student Competition, JSM 2021.)
    [arXiv].

  • On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models
    Joshua Agterberg, Minh Tang, and Carey Priebe, Submitted, 2020.
    [arXiv].

Publications

  • An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference
    Joshua Agterberg and Joshua Cape,
    Statistical Science, To Appear.
    [arXiv].

  • Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound
    Joshua Agterberg and Anru Zhang,
    Journal of the American Statistical Association, 2024+.
    [arXiv][Publisher Site].

  • Correcting a Nonparametric Two-sample Graph Hypothesis Test for Graphs with Different Numbers of Vertices with Applications to Connectomics
    Anton Alyakin, Joshua Agterberg, Hayden Helm, and Carey Priebe,
    Applied Network Science, 2024.
    [arXiv][Publisher Site].

  • Semisupervised Regression in Latent Structure Networks on Unknown Manifolds
    Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park, and Carey E. Priebe,
    Applied Network Science, 2023.
    [arXiv][Publisher Site].

  • Spectral Graph Clustering via the Expectation-Solution Algorithm
    Zachary Pisano, Joshua Agterberg, Carey Priebe, Daniel Naiman,
    Electronic Journal of Statistics, 2022.
    [arXiv][Publisher Site].

  • Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms
    Joshua Agterberg and Jeremias Sulam,
    AISTATS, 2022.
    [arXiv][Publisher Site].

  • Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
    Joshua Agterberg, Zachary Lubberts, and Carey Priebe,
    IEEE Transactions on Information Theory, 2022+.
    A recording of a talk covering this paper is available here.
    [arXiv][Publisher Site].

  • Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation: Applications in Connectomics
    Jaewon Chung, Bijan Varjavand, Jesús Arroyo, Anton Alyakin, Joshua Agterberg, Minh Tang, Joshua Vogelstein, and Carey Priebe,
    Stat, 2021.
    [arXiv][Publisher Site].

  • Vertex Nomination, Consistent Estimation, and Adversarial Modification
    Joshua Agterberg, Youngser Park, Jonathan Larson, Chris White, Carey Priebe, and Vince Lyzinski,
    Electronic Journal of Statistics, 2020.
    [arXiv][Publisher Site].

  • Social Determinant-Based Profiles of US Adults with the Highest and Lowest Health Expenditures Using Clusters
    Fanghao Zhong, Margie Rosenberg, Joshua Agterberg, and Richard Crabb,
    North American Actuarial Journal, 2020.
    [Publisher Site]

  • Cluster Analysis Application to Identify Groups of Individuals with High Health Expenditures
    Joshua Agterberg, Fanghao Zhong, Richard Crabb, and Margie Rosenberg,
    Health Services and Outcomes Research Methodology, 2020.
    [Publisher Site].

Honors and Awards

Reading Groups

Current and Previous Collaborators