两篇新研究论文探讨了机器学习共形预测的进展。第一篇论文介绍了一个公平共形分类框架，该框架保证在自适应识别的子群上进行条件覆盖，旨在减轻算法偏差。第二篇论文实验性地研究了共形电子预测器的聚合方法，重点关注现有技术的更简单、更灵活的修改，以平衡预测和计算效率。 AI
影响 这些论文推进了确保机器学习预测公平性和效率的技术，这对于值得信赖的AI系统至关重要。
排序理由 两篇在arXiv上发表的学术论文，详细介绍了共形预测的新方法。
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Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to account for algorithmic biases, thereby undermining fairness and trust. This paper introduces a fair conformal inference framework for…
Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least approximately. An important advantage of conformal e-predictors is that they are easier to aggregate without sacrificing their va…