历时样本整合：使用生成模型进行稳健的尾部风险估计
Diachronic Sample Integration: Robust Tail-Risk Estimation with Generative Models
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中文总结 AI 辅助
研究针对深度生成模型在风险敏感应用中尾部估计问题，提出历时样本整合（DSI）框架，通过整合随机训练轨迹各检查点样本，平均尾部波动，经理论形式化，实证显示其在固定预算下能大幅降低尾部估计误差，优于多种基线。
中文摘要 AI 辅助
深度生成模型越来越多地用作数据稀缺情况下下游决策的模拟器，但在风险敏感型应用中，其效用取决于罕见的不利情景而非典型样本。标准生成目标优先考虑整体分布保真度，使低概率尾部易受局部优化噪声影响，且在有限模拟预算下使尾部相关泛函不稳定。我们引入历时样本整合（DSI），这是一个测试时推理框架，它整合来自随机训练轨迹各检查点的生成样本。DSI针对检查点混合分布，平均检查点特定的尾部波动，而非依赖单个脆弱端点。我们通过有限预算偏差 - 方差理论对该机制进行形式化。实证表明，在固定模拟预算下，与单检查点基线相比，DSI在多元合成过程和高频交易数据中大幅降低尾部估计误差，优于标准扩散模型和最先进的尾部感知基线，且无需修改生成目标。
英文摘要
Deep generative models are increasingly used as simulators for downstream decision-making under data scarcity, but in risk-sensitive applications their usefulness depends on rare adverse scenarios rather than typical samples. Standard generative objectives prioritize bulk distributional fidelity, leaving low-probability tails vulnerable to localized optimization noise and making tail-dependent functionals unstable under finite simulation budgets. We introduce Diachronic Sample Integration (DSI), a test-time inference framework that ensembles generated samples across checkpoints from a stochastic training trajectory. DSI targets a checkpoint-mixture distribution that averages checkpoint-specific tail fluctuations rather than relying on a single brittle endpoint. We formalize this mechanism through a finite-budget bias-variance theory. Empirically, across multivariate synthetic processes and high-frequency trading data, DSI substantially reduces tail-estimation error compared to single-checkpoint baselines under fixed simulation budgets, outperforming standard diffusion and state-of-the-art tail-aware baselines without modifying the generative objective.
发表机构
- University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。
