论文标题
最小值的最佳分配稳定性估计
Minimax Optimal Estimation of Stability Under Distribution Shift
论文作者
论文摘要
当应用于与训练过程中看到的环境不同时，决策政策和预测模型的性能通常会恶化。为了确保可靠的操作，我们分析了正在分配偏移的系统的稳定性，该系统的稳定性定义为基础环境中最小的变化，这会导致系统性能降低超出允许的阈值。与需要规格的合理分配变化规范的标准尾巴风险度量和分配强大的损失相反，稳定性度量是根据更直观的数量来定义的：可接受的性能退化水平。我们开发了最小的稳定性最佳估计量，并分析其收敛速率，该估计率表现出基本的相位转移行为。我们对最小值收敛速率的表征表明，评估稳定性对较大的性能降解会导致统计成本。从经验上讲，我们通过使用稳定性转移至关重要的问题来比较系统设计，从而证明了稳定性框架的实际实用性。
The performance of decision policies and prediction models often deteriorates when applied to environments different from the ones seen during training. To ensure reliable operation, we analyze the stability of a system under distribution shift, which is defined as the smallest change in the underlying environment that causes the system's performance to deteriorate beyond a permissible threshold. In contrast to standard tail risk measures and distributionally robust losses that require the specification of a plausible magnitude of distribution shift, the stability measure is defined in terms of a more intuitive quantity: the level of acceptable performance degradation. We develop a minimax optimal estimator of stability and analyze its convergence rate, which exhibits a fundamental phase shift behavior. Our characterization of the minimax convergence rate shows that evaluating stability against large performance degradation incurs a statistical cost. Empirically, we demonstrate the practical utility of our stability framework by using it to compare system designs on problems where robustness to distribution shift is critical.
