解决模拟推断中缺失数据导致的偏差问题，提升模型鲁棒性。
Robust Simulation-Based Inference under Missing Data via Neural Processes
- 联合学习插补与推断，避免传统插补带来的误差。
- 在多种缺失率下均优于基准方法，结果更稳定可靠。
- 适用于天体物理、高能物理及生物活性数据等真实场景。
模拟推断（SBI）方法通常需要完整观测数据来推断似然函数不可计算模型的参数。然而，由于观测不全、数据损坏（如天体物理中常见）或仪器限制（如高能物理应用），数据集常含缺失值。此时需先插补缺失数据才能应用SBI方法。本文正式定义了SBI中的缺失数据问题，并证明了朴素插补方法会引入后验估计偏差。为此，提出一种新型摊销方法，在神经后验估计（NPE）框架内联合学习插补模型与推理网络。在多个SBI基准测试上，该方法在不同缺失率下均表现出更强的鲁棒性。此外，在两个真实生物活性数据集（Adrenergic和Kinase测定）上也验证了其有效性。代码已开源：https://github.com/Aalto-QuML/RISE。
原文摘要 · Abstract (English)
Simulation-based inference (SBI) methods typically require fully observed data to infer parameters of models with intractable likelihood functions. However, datasets often contain missing values due to incomplete observations, data corruptions (common in astrophysics), or instrument limitations (e.g., in high-energy physics applications). In such scenarios, missing data must be imputed before applying any SBI method. We formalize the problem of missing data in SBI and demonstrate that naive imputation methods can introduce bias in the estimation of SBI posterior. We also introduce a novel amortized method that addresses this issue by jointly learning the imputation model and the inference network within a neural posterior estimation (NPE) framework. Extensive empirical results on SBI benchmarks show that our approach provides robust inference outcomes compared to standard baselines for varying levels of missing data. Moreover, we demonstrate the merits of our imputation model on two real-world bioactivity datasets (Adrenergic and Kinase assays). Code is available at https://github.com/Aalto-QuML/RISE.
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