[33] Andrew D McRae, Justin Romberg, and Mark A Davenport. Optimal convex lifted sparse phase retrieval and PCA with an atomic matrix norm regularizer. IEEE Transactions on Information Theory, 69(3):1866-1882, 2022.
[34] Po-Ling Loh. Statistical consistency and asymptotic normality for high-dimensional robust  $M$ -estimators. The Annals of Statistics, 45(2):866-896, 2017. doi: 10.1214/16-AOS1471.
[35] Jianqing Fan, Quefeng Li, and Yuyan Wang. Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. Journal of the Royal Statistical Society Series B, Statistical methodology, 79(1):247-265, 2016.
[36] Arkadj Semenovic Nemirovskij and David Borisovich Yudin. Problem complexity and method efficiency in optimization. Wiley-Interscience, 1983.
[37] Stanislav Minsker. Geometric median and robust estimation in Banach spaces. Bernoulli, 21(4): 2308-2335, 2015.
[38] Daniel Hsu and Sivan Sabato. Loss minimization and parameter estimation with heavy tails. Journal of Machine Learning Research, 17(1):543-582, 2016.
[39] Jianqing Fan, Weichen Wang, and Ziwei Zhu. A shrinkage principle for heavy-tailed data: High-dimensional robust low-rank matrix recovery. The Annals of Statistics, 49(3):1239-1266, 2021.
[40] Austin Xu and Mark Davenport. Simultaneous preference and metric learning from paired comparisons. Advances in Neural Information Processing Systems, 33, 2020.
[41] Gregory Canal, Blake Mason, Ramya Korlakai Vinayak, and Robert Nowak. One for all: Simultaneous metric and preference learning over multiple users. In Advances in Neural Information Processing Systems, volume 35, 2022.
[42] Sahand Negahban and Martin J Wainwright. Restricted strong convexity and weighted matrix completion: Optimal bounds with noise. Journal of Machine Learning Research, 13(1):1665-1697, 2012.
[43] Steven Diamond and Stephen Boyd. CVXPY: A Python-embedded modeling language for convex optimization. Journal of Machine Learning Research, 17(83):1-5, 2016.
[44] Akshay Agrawal, Robin Verschueren, Steven Diamond, and Stephen Boyd. A rewriting system for convex optimization problems. Journal of Control and Decision, 5(1):42-60, 2018.
[45] Andrew K Massimino and Mark A Davenport. As you like it: Localization via paired comparisons. Journal of Machine Learning Research, 22(1):8357-8395, 2021.
[46] Roman Vershynin. High-dimensional probability: An introduction with applications in data science, volume 47. Cambridge university press, 2018.
[47] Stéphane Boucheron, Gábor Lugosi, and Pascal Massart. Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford University Press, 2013. ISBN 9780199535255. doi: 10.1093/acprof:oso/9780199535255.001.0001.
[48] Yong Bao and Raymond Kan. On the moments of ratios of quadratic forms in normal random variables. Journal of Multivariate Analysis, 117:229-245, 2013.
[49] Shahar Mendelson. Learning without concentration. Journal of the ACM (JACM), 62(3):1-25, 2015.
[50] Joel A Tropp. Convex recovery of a structured signal from independent random linear measurements. Sampling Theory, a Renaissance: Compressive Sensing and Other Developments, pages 67-101, 2015.
[51] Mark Rudelson and Roman Vershynin. Hanson-Wright inequality and sub-gaussian concentration. Electronic Communications in Probability, 18:1-9, 2013.
[52] Roman Vershynin. Introduction to the non-asymptotic analysis of random matrices. arXiv preprint arXiv:1011.3027, 2010.