Vertex Nomination, Consistent Estimation, and Adversarial Modification. - Joshua Agterberg; Youngser Park; Jonathan Larson; Christopher M. White; Carey E. Priebe; Vince Lyzinski (CoRR, 2019)

Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms. - Joshua Agterberg; Jeremias Sulam (AISTATS, 2022)

Entrywise Estimation of Singular Vectors of Low-Rank Matrices With Heteroskedasticity and Dependence. - Joshua Agterberg; Zachary Lubberts; Carey E. Priebe (IEEE Trans. Inf. Theory, 2022)

Semisupervised regression in latent structure networks on unknown manifolds. - Aranyak Acharyya; Joshua Agterberg; Michael W. Trosset; Youngser Park; Carey E. Priebe (Appl. Netw. Sci., 2023)

ICL-TSVD: Bridging Theory and Practice in Continual Learning with Pre-trained Models. - Liangzu Peng; Juan Elenter; Joshua Agterberg; Alejandro Ribeiro; René Vidal (CoRR, 2024)

Correcting a nonparametric two-sample graph hypothesis test for graphs with different numbers of vertices with applications to connectomics. - Anton Alyakin; Joshua Agterberg; Hayden S. Helm; Carey E. Priebe (Appl. Netw. Sci., 2024)

A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks. - Uday Kiran Reddy Tadipatri; Benjamin David Haeffele; Joshua Agterberg; René Vidal (AISTATS, 2025)

LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning. - Liangzu Peng; Juan Elenter; Joshua Agterberg; Alejandro Ribeiro; René Vidal (ICLR, 2025)

Nonconvex Linear System Identification with Minimal State Representation. - Uday Kiran Reddy Tadipatri; Benjamin D. Haeffele; Joshua Agterberg; Ingvar M. Ziemann; René Vidal (L4DC, 2025)