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We propose SGD-exp, a stochastic gradient descent approach for linear and ReLU regressions under Massart noise (adversarial semi-random corruption model) for the fully streaming setting.
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Quantile-based iterative methods for corrupted systems of linear equations
Jamie Haddock, Deanna Needell, Elizaveta Rebrova, and William Swartworth · 2022
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Distribution-independent regression for generalized linear models with oblivious corruptions
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On subsampled quantile randomized Kaczmarz
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On the convergence of stochastic gradient descent with bandwidth-based step size
Xiaoyu Wang and Ya-xiang Yuan · 2023
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Finite-sample analysis of learning high-dimensional single ReLU neuron
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, and Sham M Kakade · 2023
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