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We investigate robust linear regression where data may be contaminated by an oblivious adversary, i.e., an adversary than may know the data distribution but is otherwise oblivious to the realizations of the data samples.
High dimensional robust m m -estimation: Arbitrary corruption and heavy tails
L. Liu, T. Li, and C. Caramanis · 1901
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A. S. Dalalyan and P. Thompson · 1904
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Econometric applications of high-breakdown robust regression techniques
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E. Tsakonas, J. Jalden, N. D. Sidiropoulos, and B. Ottersten · 2014
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Black-box reductions for parameter-free online learning in banach spaces
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High-dimensional probability : An introduction with applications in data science
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Adaptive hard thresholding for near-optimal consistent robust regression
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Robust estimators in high-dimensions without the computational intractability
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Efficient algorithms and lower bounds for robust linear regression
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Least squares, 1809
C. F. Gauss
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