Computationally Efficient Robust Sparse Estimation in High Dimensions

By Sivaraman Balakrishnan, Simon Shaolei Du, Jerry Zheng Li, Aarti Singh. 2017

132 citations recorded by the source corpus.

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Citations in the corpus

Spectral Signatures in Backdoor Attacks (2018, background): “Another line of work focuses on applying the robust statistics tools developed in [8, 22, 5, 9] to robust stochastic optimization problems [2, 5, 10, 17, 27].”
Sever: A Robust Meta-Algorithm for Stochastic Optimization (2018, background): “On the other hand, [BDLS17] studies (sparse) linear regression and, more broadly, generalized linear models (GLMs) under a robustness model very similar to the one considered here.”
Efficient Algorithms and Lower Bounds for Robust Linear Regression (2018, methodology): “(3) Moreover, the [BDLS17] algorithm assumes a priori knowledge of an upper bound ρ on β 2 , and its running time depends polynomially in the magnitude of ρ .”
Choosing the Sample with Lowest Loss makes SGD Robust (2020, background): “Another line of recent work considers robustness in the high-dimensional setting ([27, 36, 8, 3, 24]) In this version, our focus is on general stochastic optimization in presence of outliers.”
Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers (2017, background): “(2017a) provide a case study of various robust estimation methods in a genomic setting, Balakrishnan et al. (2017) study sparse mean estimation, and others have studied problems including regression, Bayes nets, planted clique, and several other settings (Diakonikolas et al., 2016b,c, 2017b,c; Kane ”
List-decodable robust mean estimation and learning mixtures of spherical gaussians (2017, methodology): “This includes robust estimation of graphical models [16], handling a large fraction of corruptions in the list-decodable model [11, 43], developing robust algorithms under sparsity assumptions [5], obtaining optimal error guarantees [15], establishing computational lower bounds for robust estimation”
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks (2019, methodology): “In the context of robust regression and high-dimensional statistics, much of the focus is on regularization techniques to automatically detect and discard outliers by using tools such as `1 penalization [6, 10,15,17,22,32,35].”
Learning with Bad Training Data via Iterative Trimmed Loss Minimization (2018, background): “Another line of recent work on robust regression consider strong robustness where the adversary poisons both the inputs and outputs, in both low-dimensional [DKK + 18, PSBR18, KKM18] and high dimensional [CCM13, BDLS17, LSLC18] settings.”