这篇论文的DSI方法用多检查点集成来稳定尾部风险估计，实验效果比单检查点好不少，搞金融风控的值得一看。
生成模型在数据稀缺下的决策模拟中越来越常用，但风险敏感应用依赖罕见尾部场景。标准生成目标优化整体分布保真度，低概率尾部易受局部优化噪声影响。论文提出Diachronic Sample Integration (DSI)，一种测试时推理框架，集成来自随机训练轨迹不同检查点的生成样本。DSI通过有限预算偏差-方差理论形式化，并在多变量合成过程和高频交易数据上，相比单检查点基线显著降低尾部估计误差，优于标准扩散和最先进尾部感知基线。
Diachronic Sample Integration: Robust Tail-Risk Estimation with Generative Models
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.