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Big data are often contaminated by outliers and heavy-tailed errors,which are also a stylized feature of high-dimensional data.To address this challenge,we propose the adaptive Huber regression for robust estimation and inference.The key observation is that the robustification parameter should adapt to the sample size,dimension and moments for optimal tradeoff between biases and robustness.