根据输入动态调整模型权重，提升预测准确性和可靠性
Input Adaptive Bayesian Model Averaging
- 基于输入自适应的贝叶斯平均，动态分配模型权重
- 在癌症治疗、欺诈检测等任务中误差更低，校准更优
- 适合输入差异大的复杂场景，如个性化医疗与金融风控
本文研究多模型联合预测问题，目标是融合多个候选模型的输出。在异质场景下，不同模型对不同输入表现各异，此任务尤为挑战。我们提出输入自适应贝叶斯模型平均（IA-BMA），一种基于输入条件的贝叶斯方法，可动态分配模型权重。该方法采用输入自适应先验，通过摊销变分推断估计随输入变化的后验分布，并提供相对于每输入最优单模型的性能保证。我们在回归与分类任务中评估了IA-BMA，涵盖个性化癌症治疗、信用卡欺诈检测及UCI数据集。结果表明，IA-BMA在所有任务中均持续优于非自适应基线和现有自适应方法，预测更准确且校准更佳。
原文摘要 · Abstract (English)
This paper studies prediction with multiple candidate models, where the goal is to combine their outputs. This task is especially challenging in heterogeneous settings, where different models may be better suited to different inputs. We propose input adaptive Bayesian Model Averaging (IA-BMA), a Bayesian method that assigns model weights conditional on the input. IA-BMA employs an input adaptive prior, and yields a posterior distribution that adapts to each prediction, which we estimate with amortized variational inference. We derive formal guarantees for its performance, relative to any single predictor selected per input. We evaluate IABMA across regression and classification tasks, studying data from personalized cancer treatment, credit-card fraud detection, and UCI datasets. IA-BMA consistently delivers more accurate and better-calibrated predictions than both non-adaptive baselines and existing adaptive methods.
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