Additional Campus Affiliations
Assistant Professor, Statistics
External Links
Highlighted Publications
Agterberg, J., & Zhang, A. R. (2025). Estimating Higher-Order Mixed Memberships via the l2,∞ Tensor Perturbation Bound. Journal of the American Statistical Association, 120(550), 1214-1224. https://doi.org/10.1080/01621459.2024.2404265
Agterberg, J., Lubberts, Z., & Priebe, C. E. (2022). Entrywise Estimation of Singular Vectors of Low-Rank Matrices With Heteroskedasticity and Dependence. IEEE Transactions on Information Theory, 68(7), 4618-4650. https://doi.org/10.1109/TIT.2022.3159085
Agterberg, J., & Sulam, J. (2022). Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms. Proceedings of Machine Learning Research, 151, 6591-6629.
Recent Publications
Agterberg, J. (2026). Joshua Agterberg’s Discussion of ‘Statistical exploration of the manifold hypothesis’ by Whiteley et al. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 88(2), 400-402. https://doi.org/10.1093/jrsssb/qkag038
Agterberg, J., & Zhang, A. R. (2025). Estimating Higher-Order Mixed Memberships via the l2,∞ Tensor Perturbation Bound. Journal of the American Statistical Association, 120(550), 1214-1224. https://doi.org/10.1080/01621459.2024.2404265
Agterberg, J., Lubberts, Z., & Arroyo, J. (2025). Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels. Journal of the American Statistical Association, 120(551), 1607-1620. https://doi.org/10.1080/01621459.2025.2516201
Peng, L., Litwin, J. E., Agterberg, J., Ribeiro, A., & Vidal, R. (2025). LORANPAC: LOW-RANK RANDOM FEATURES AND PRE-TRAINED MODELS FOR BRIDGING THEORY AND PRACTICE IN CONTINUAL LEARNING. In 13th International Conference on Learning Representations, ICLR 2025 (pp. 23483-23529). (13th International Conference on Learning Representations, ICLR 2025). International Conference on Learning Representations, ICLR.
Tadipatri, U. K. R., Haeffele, B. D., Agterberg, J., & Vidal, R. (2025). A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks. Proceedings of Machine Learning Research, 258, 5239-5247.