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In this book, we systematically explore the core problems and solutions of trustworthy machine learning, covering key areas from noisy label, adversarial samples, and out-of-distribution samples to federated learning, graph learning, causal reasoning, and trustworthy foundation models.

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Conclusion and Prospects

  • 章节

  • 发表日期：

    2026年

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  • Bo Han,
  • Tongliang Liu

摘要

In this book, we systematically explore the core problems and solutions of trustworthy machine learning, covering key areas from noisy label, adversarial samples, and out-of-distribution samples to federated learning, graph learning, causal reasoning, and trustworthy foundation models.