This monograph provides a systematic overview of the research problems under trustworthy machine learning, covering the perspectives from data to model. Starting with fundamental data-centric learning, this work reviews learning with noisy data, long-tailed distribution, out-of-distribution data, and adversarial examples to achieve robustness.
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This monograph provides a systematic overview of the research problems under trustworthy machine learning, covering the perspectives from data to model. Starting with fundamental data-centric learning, this work reviews learning with noisy data, long-tailed distribution, out-of-distribution data, and adversarial examples to achieve robustness.
Read Less
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