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(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems.
Dawid AP, Skene AM (1979) Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics) 28(1):20–28
1979
Earlier work this paper cites.
Wu CJ, et al. (1983) On the convergence properties of the em algorithm. The Annals of statistics 11(1):95–103
1983
Earlier work this paper cites.
Catoni O (2004) Statistical learning theory and stochastic optimization: Ecole d’Eté de Probabilités de Saint-Flour, XXXI-2001, vol 1851. Springer Science & Business Media
2001
Earlier work this paper cites.
Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Theory of cryptography conference, Springer, pp 265–284
2006
Earlier work this paper cites.
McLachlan G, Krishnan T (2007) The EM algorithm and extensions, vol 382. John Wiley & Sons
2007
Earlier work this paper cites.
Nissim K, Raskhodnikova S, Smith A (2007) Smooth sensitivity and sampling in private data analysis. In: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pp 75–84
2007
Earlier work this paper cites.
Laird NM (2010) The em algorithm in genetics, genomics and public health. Statistical Science pp 450–457
2010
Earlier work this paper cites.
Vershynin R (2010) Introduction to the non-asymptotic analysis of random matrices. arXiv preprint arXiv:10113027
2010
Earlier work this paper cites.
Loh PL, Wainwright MJ (2011) High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. In: Advances in Neural Information Processing Systems, pp 2726–2734
2011
Earlier work this paper cites.
Boucheron S, Lugosi G, Massart P (2013) Concentration inequalities: A nonasymptotic theory of independence. Oxford university press
2013
Earlier work this paper cites.
Faria S, Gonçalves F (2013) Financial data modeling by poisson mixture regression. Journal of Applied Statistics 40(10):2150–2162
2013
Cited alongside, same era.
Nesterov Y (2013) Introductory lectures on convex optimization: A basic course, vol 87. Springer Science & Business Media
2013
Cited alongside, same era.
Song S, Chaudhuri K, Sarwate AD (2013) Stochastic gradient descent with differentially private updates. In: 2013 IEEE Global Conference on Signal and Information Processing, IEEE, pp 245–248
2013
Cited alongside, same era.
Bassily R, Smith A, Thakurta A (2014) Private empirical risk minimization: Efficient algorithms and tight error bounds. In: 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, IEEE, pp 464–473
2014
Cited alongside, same era.
Dwork C, Roth A, et al. (2014) The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9(3-4):211–407
Park M, Foulds J, Choudhary K, Welling M (2017) Dp-em: Differentially private expectation maximization. In: Artificial Intelligence and Statistics, pp 896–904
2017
Later among the works it cites.
Wang D, Ye M, Xu J (2017) Differentially private empirical risk minimization revisited: Faster and more general. In: Advances in Neural Information Processing Systems, pp 2722–2731
2017
Later among the works it cites.
Zhu R, Wang L, Zhai C, Gu Q (2017) High-dimensional variance-reduced stochastic gradient expectation-maximization algorithm. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, pp 4180–4188
2017
Later among the works it cites.
Lee J, Kifer D (2018) Concentrated differentially private gradient descent with adaptive per-iteration privacy budget. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1656–1665
2018
Later among the works it cites.
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2014
Cited alongside, same era.
Wang Z, Gu Q, Ning Y, Liu H (2015) High dimensional em algorithm: Statistical optimization and asymptotic normality. In: Advances in neural information processing systems, pp 2521–2529
2015
Cited alongside, same era.
Yi X, Caramanis C (2015) Regularized em algorithms: A unified framework and statistical guarantees. In: Advances in Neural Information Processing Systems, pp 1567–1575
2015
Cited alongside, same era.
Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. In: CCS, pp 308–318
2016
Cited alongside, same era.
Bun M, Steinke T (2016) Concentrated differential privacy: Simplifications, extensions, and lower bounds. In: Theory of Cryptography Conference, Springer, pp 635–658
2016
Cited alongside, same era.
Catoni O, Giulini I (2017) Dimension-free pac-bayesian bounds for matrices, vectors, and linear least squares regression. arXiv preprint arXiv:171202747
2017
Cited alongside, same era.
Balakrishnan S, Du SS, Li J, Singh A (2017a) Computationally efficient robust sparse estimation in high dimensions. In: Conference on Learning Theory, pp 169–212
Cited in the paper.
Balakrishnan S, Wainwright MJ, Yu B, et al. (2017b) Statistical guarantees for the em algorithm: From population to sample-based analysis. The Annals of Statistics 45(1):77–120
Cited in the paper.
Holland MJ (2019) Robust descent using smoothed multiplicative noise. In: 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research, vol 89, pp 703–711
2019
Later among the works it cites.
Kamath G, Sheffet O, Singhal V, Ullman J (2019) Differentially private algorithms for learning mixtures of separated gaussians. In: Advances in Neural Information Processing Systems, pp 168–180
2019
Later among the works it cites.
Wang D, Xu J (2019) Differentially private empirical risk minimization with smooth non-convex loss functions: A non-stationary view. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 1182–1189
2019
Later among the works it cites.
Brunel VE, Avella-Medina M (2020) Propose, test, release: Differentially private estimation with high probability. arXiv preprint arXiv:200208774
2020
Closest in time.
Song S, Thakkar O, Thakurta A (2020) Characterizing private clipped gradient descent on convex generalized linear problems. arXiv preprint arXiv:200606783
2020
Closest in time.
Wang D, Xiao H, Devadas S, Xu J (2020) On differentially private stochatsic optimization with heavy-tailed data. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 12-18 July 2020, Virtual Conference
2020
Closest in time.