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Updated: Mar 13, 2026

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对任意序列进行校准的概率预测
Charles Marx1, Volodymyr Kuleshov2, Stefano Ermon1
1Department of Computer Science, Stanford University.
概括
本研究引入了一种使用游戏理论的新预测框架,以确保可靠的不确定性估计,即使有不可预测的数据变化. 该方法保证了校准的预测,并改善了在能源系统等现实应用中的决策.
科学领域:
- 机器学习 机器学习
- 游戏理论 游戏理论
- 数据科学数据科学数据科学
背景情况:
- 现实世界的数据流面临着不可预测的变化 (分布转移,反循环,对抗性行为者).
- 这些变化挑战了现有的预测方法的有效性和可靠性.
- 确保准确的不确定性估计对于可靠的决策至关重要.
研究的目的:
- 开发一个预测框架,提供有效的不确定性估计,无论数据的演变.
- 为了保证在紧的空间中对结果的校准不确定性.
- 扩大用于重新校准现有预测器的框架,而不会造成性能损失.
主要方法:
- 从游戏理论中利用布莱克韦尔的可接近性.
- 开发一种基于梯度的通用算法.
- 为框架的特殊情况优化算法.
- 为现有预测者实施重新校准技术.
主要成果:
- 该框架为任何紧的结果空间保证了校准的不确定性.
- 再校准的预报器在不牺牲预测性能的情况下实现校准.
- 经验结果表明,能源系统的校准和决策得到了改进.
结论:
- 拟议的框架确保在动态数据环境中有效估计不确定性.
- 这种方法提高了预测和下游决策的可靠性.
- 这些方法适用于各种预测任务,包括分类和局限回归.
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