[47] Elchanan Mossel, Joe Neeman, and Allan Sly. Consistency thresholds for the planted bisection model. In Proceedings of the forty-seventh annual ACM symposium on Theory of computing, pages 69-75, 2015.
[48] Elchanan Mossel, Joe Neeman, and Allan Sly. Reconstruction and estimation in the planted partition model. Probability Theory and Related Fields, 162(3):431-461, 2015.
[49] Elchanan Mossel, Joe Neeman, and Allan Sly. A proof of the block model threshold conjecture. Combinatorica, 38(3):665-708, 2018.
[50] Andres Munoz, Umar Syed, Sergei Vassilvtiskii, and Ellen Vitercik. Private optimization without constraint violations. In International Conference on Artificial Intelligence and Statistics, pages 2557-2565. PMLR, 2021.
[51] Yurii Nesterov. Squared functional systems and optimization problems. In High performance optimization, volume 33 of Appl. Optim., pages 405-440. Kluwer Acad. Publ., Dordrecht, 2000.
[52] Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. Smooth sensitivity and sampling in private data analysis. In STOC'07—Proceedings of the 39th Annual ACM Symposium on Theory of Computing, pages 75–84. ACM, New York, 2007.
[53] Pablo A Parrilo. Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization. PhD thesis, California Institute of Technology, 2000.
[54] Oded Regev and Aravindan Vijayaraghavan. On learning mixtures of well-separated gaussians. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), pages 85-96. IEEE, 2017.
[55] Mohamed M. Seif, Dung Nguyen, Anil Vullikanti, and Ravi Tandon. Differentially private community detection for stochastic block models. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato, editors, International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 15858-15894. PMLR, 2022.
[56] N. Z. Shor. Quadratic optimization problems. Izv. Akad. Nauk SSSR Tekhn. Kibernet., (1):128-139, 222, 1987.
[57] Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate. Stochastic gradient descent with differentially private updates. In 2013 IEEE global conference on signal and information processing, pages 245-248. IEEE, 2013.
[58] David Steurer. Fast sdp algorithms for constraint satisfaction problems. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms, pages 684-697. SIAM, 2010.
[59] David Steurer and Stefan Tiegel. Sos degree reduction with applications to clustering and robust moment estimation. In Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 374-393. SIAM, 2021.
[60] Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, and Uri Stemmer. Friendlycore: Practical differentially private aggregation. In International Conference on Machine Learning, pages 21828-21863. PMLR, 2022.
[61] Jonathan Ullman and Adam Sealfon. Efficiently estimating ergos-renyi graphs with node differential privacy. Advances in Neural Information Processing Systems, 32, 2019.
[62] Martin J. Wainwright. High-Dimensional Statistics: A Non-Asymptotic Viewpoint. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 2019.
[63] Di Wang, Minwei Ye, and Jinhui Xu. Differentially private empirical risk minimization revisited: Faster and more general. Advances in Neural Information Processing Systems, 30, 2017.
[64] Anderson Y Zhang and Harrison H Zhou. Minimax rates of community detection in stochastic block models. The Annals of Statistics, 44(5):2252-2280, 2016.