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A new type of robust estimation problem is introduced where the goal is to recover a statistical model that has been corrupted after it has been estimated from data.
Two models of double descent for weak features
Belkin, M., Hsu, D., and Xu, J. (2019) · 1903
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Surprises in high-dimensional ridgeless least squares interpolation
Hastie, T., Montanari, A., Rosset, S., and Tibshirani, R. J. (2019) · 1903
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Suggala, A. S., Bhatia, K., Ravikumar, P., and Jain, P. (2019) · 1903
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The generalization error of random features regression: Precise asymptotics and double descent curve
Mei, S. and Montanari, A. (2019) · 1908
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Recent advances in algorithmic high-dimensional robust statistics
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Robust covariance and scatter matrix estimation under huber’s contamination model
Chen, M., Gao, C., and Ren, Z. (2018) · 1960
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Probability inequalities for sums of bounded random variables
Hoeffding, W. (1963) · 1963
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Huber, P. J. (1964) · 1964
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Cirel’son, B. S., Ibragimov, I. A., and Sudakov, V. (1976) · 1976
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Adaptive estimation of a quadratic functional by model selection
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Local operator theory, random matrices and Banach spaces
Davidson, K. R. and Szarek, S. J. (2001) · 2001
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Convex Optimization
Boyd, S. and Vandenberghe, L. (2004) · 2004
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Elements of Information Theory
Cover, T. and Thomas, J. (2006) · 2006
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A second course in probability
Ross, S. M. and Peköz, E. A. (2007) · 2007
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Rahimi, A. and Recht, B. (2008) · 2008
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Wright, J. A. and Ma, Y. (2010) · 2010
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Secure and efficient distributed linear programming
Hong, Y., Vaidya, J., and Lu, H. (2012) · 2012
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Least squares superposition codes of moderate dictionary size are reliable at rates up to capacity
Joseph, A. and Barron, A. R. (2012) · 2012
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Consistent robust regression
Bhatia, K., Jain, P., Kamalaruban, P., and Kar, P. (2017) · 2017
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Being robust (in high dimensions) can be practical
Diakonikolas, I., Kamath, G., Kane, D. M., Li, J., Moitra, A., and Stewart, A. (2017) · 2017
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Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2017) · 2017
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Capacity-achieving sparse superposition codes via approximate message passing decoding
Rush, C., Greig, A., and Venkataramanan, R. (2017) · 2017
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A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y., and Song, Z. (2018) · 2018
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A new perspective on least squares under convex constraint
Chatterjee, S. (2014) · 2014
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Generative adversarial nets
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In search of the real inductive bias: On the role of implicit regularization in deep learning
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Convergence of the huber regression m-estimate in the presence of dense outliers
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Sharp oracle bounds for monotone and convex regression through aggregation
Bellec, P. C. and Tsybakov, A. B. (2015) · 2015
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On risk bounds in isotonic and other shape restricted regression problems
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Neural tangent kernel: Convergence and generalization in neural networks
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Compressed sensing with adversarial sparse noise via l1 regression
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P. (2018) · 2018
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Robust estimation via generative adversarial networks
Gao, C., Liu, J., Yao, Y., and Zhu, W. (2019) · 2019
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Benign overfitting in linear regression
Bartlett, P. L., Long, P. M., Lugosi, G., and Tsigler, A. (2020) · 2020
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Robust regression via mutivariate regression depth
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Exact recoverability from dense corrupted observations via ℓ 1 \ell_{1} minimization
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Robust lasso with missing and grossly corrupted observations
Nguyen, N. H. and Tran, T. D. (2013b) · 2056
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