Farias, V., Li, A. A., and Peng, T. (2022). Uncertainty quantification for low-rank matrix completion with heterogeneous and sub-exponential noise. In International Conference on Artificial Intelligence and Statistics, pages 1179-1189. PMLR.
Fazel, M., Hindi, H., and Boyd, S. (2004). Rank minimization and applications in system theory. In Proceedings of the 2004 American control conference, volume 4, pages 3273-3278. IEEE.
Foygel, R. and Srebro, N. (2011). Concentration-based guarantees for low-rank matrix reconstruction. In Kakade, S. M. and von Luxburg, U., editors, Proceedings of the 24th Annual Conference on Learning Theory, volume 19 of Proceedings of Machine Learning Research, pages 315-340, Budapest, Hungary. PMLR.
He, X. and Shao, Q.-M. (2000). On parameters of increasing dimensions. Journal of multivariate analysis, 73(1):120-135.
Jain, P., Netrapalli, P., and Sanghavi, S. (2013). Low-rank matrix completion using alternating minimization. In Proceedings of the forty-fifth annual ACM symposium on Theory of computing, pages 665-674.
Jin, Y., Ren, Z., and Candès, E. J. (2023). Sensitivity analysis of individual treatment effects: A robust conformal inference approach. Proceedings of the National Academy of Sciences, 120(6):e2214889120.
Keshavan, R. H., Montanari, A., and Oh, S. (2010). Matrix completion from a few entries. IEEE transactions on information theory, 56(6):2980-2998.
Koltchinskii, V., Lounici, K., and Tsybakov, A. B. (2011). Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion. The Annals of Statistics, 39(5):2302 - 2329.
Lei, J., G'Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018). Distribution-free predictive inference for regression. Journal of the American Statistical Association, 113(523):1094-1111.
Lei, L. and Candès, E. J. (2020). Conformal inference of counterfactuals and individual treatment effects. arXiv preprint arXiv:2006.06138.
Ma, C., Wang, K., Chi, Y., and Chen, Y. (2020). Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution. Foundations of Computational Mathematics, 20:451-632.
Nair, J., Wierman, A., and Zwart, B. (2022). The fundamentals of heavy tails: Properties, emergence, and estimation, volume 53. Cambridge University Press.
Natarajan, N. and Dhillon, I. S. (2014). Inductive matrix completion for predicting gene-disease associations. Bioinformatics, 30(12):i60-i68.
Negahban, S. and Wainwright, M. J. (2012). Restricted strong convexity and weighted matrix completion: Optimal bounds with noise. The Journal of Machine Learning Research, 13(1):1665-1697.
Portnoy, S. (1988). Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity. The Annals of Statistics, pages 356-366.
Rennie, J. D. and Srebro, N. (2005). Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd international conference on Machine learning, pages 713-719.
Romano, Y., Sesia, M., and Candes, E. (2020). Classification with valid and adaptive coverage. Advances in Neural Information Processing Systems, 33:3581-3591.
Shafer, G. and Vovk, V. (2008). A tutorial on conformal prediction. Journal of Machine Learning Research, 9(3).