Robust Logistic Regression and Classification

By Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan. 2014

177 citations recorded by the source corpus.

Subfields: Statistics and Probability, Statistical Learning Theory

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In Statistical Learning Theory

Citations in the corpus

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning (2019, background): “Another category of defenses [20, 30, 35, 55] proposed new loss functions, optimizing which obtains model parameters and detects the injected malicious data simultaneously.”
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning (2017, methodology): “Such statistical robust learning algorithms have been proposed for several traditional machine learning models such as linear and logistic regressions [14], [18], [27], [16], [39], [61].”
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data (2017, unknown): “, data sanitization [16], robust loss functions [51, 53], and robust learning [14, 20]), and adversarial learning [9, 17, 19] are necessary to secure data-driven CPS, but they are not sucient.”
Robust High-Dimensional Linear Regression (2016, background): “Recently, Chen et al. [9] and Feng et al. [10] considered recovery strategy when the corruption is adversarially chosen to achieve some malicious goal.”
ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “proposed a variant of logistic regression that is robust to outliers [15].”
Distributionally Robust Logistic Regression (2015, background): “As in [2], we set n = 10 and assume that the features follow a multivariate standard normal distribution, while the conditional distribution of the labels is of the form (1) with β sampled uniformly from the unit sphere.”
Consistent Robust Regression (2017, background): “The problem has been studied in a dizzying variety of models and settings ranging from regression [19], classiﬁcation [11], dimensionality reduction [4] and matrix completion [8].”
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, methodology): “Literature Model-protected Strategy Adversarial Setting Year Techniques Goals [4] cross-models data-driven cross-attacks UnT 2010 [33] logistic regression data-driven DM UnT 2014 [72] cross-models data-driven LM UnT 2015 [99] cross-models data-driven LM UnT 2018 [98] cross-models data-driven cross-a”
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, unknown): “Robust Logistic Regression [14].”
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection (2018, methodology): “Furthermore, the algorithm does not require to know the fraction of poisoning points in advance, as it is the case in [19].”
Reweighted Data for Robust Probabilistic Models (2016, background): “The ﬁrst is a rich body of work on robust statistics and machine learning (Provost and Fawcett, 2001; Song et al., 2002; Yu et al., 2012; McWilliams et al., 2014; Feng et al., 2014; Shaﬁeezadeh-Abadeh et al., 2015).”
Robust Linear Regression Against Training Data Poisoning (2017, background): “Recently, robust learning for several learning models, such as linear and logistic regression have also been proposed [10, 14].”
A General Method for Robust Bayesian Modeling (2015, methodology): “Closer to our method is the shift model of Tibshirani and Manning (2013), who use L 1 regularization, as well as the more recent theoretical work of Feng et al. (2014).”