Real-Time Auction (RTA) Interception aims to filter out invalid or irrelevant traffic to enhance the integrity and reliability of downstream data. However, two key challenges remain: (i) the need for accurate estimation of traffic quality together with sufficiently high confidence in the model's predictions, typically addressed through uncertainty modeling, and (ii) the efficiency bottlenecks that such uncertainty modeling introduces in real-time applications due to repeated inference. To address these challenges, we first provide a theoretical analysis of the intrinsic mechanism underlying uncertainty estimation. Building on this analysis, we propose a joint modeling framework that integrates multi-objective learning with uncertainty modeling, named UMDA, which yields both traffic quality predictions and reliable confidence estimates. We further apply knowledge distillation to UMDA, enabling the model to produce both aleatoric and epistemic uncertainties in a single forward pass, thereby substantially reducing the computational overhead of uncertainty modeling, while largely preserving predictive accuracy and retaining the benefits of multiple-forward-pass uncertainty estimation. Experiments on the JD and Criteo datasets demonstrate that UMDA provides more effective samples for downstream tasks through uncertainty sharing, and the distilled model retains the original uncertainty-sharing capability while delivering a tenfold increase in inference speed.

翻译：实时竞价拦截旨在过滤无效或无关流量，以增强下游数据的完整性和可靠性。然而，当前仍面临两个关键挑战：(i) 需要准确估计流量质量，同时对模型预测保持足够高的置信度——这通常通过不确定性建模实现；(ii) 这种不确定性建模因需要重复推理而在实时应用中引入效率瓶颈。针对这些挑战，我们首先对不确定性估计的内在机理进行了理论分析。在此基础上，提出了一种联合建模框架UMDA，将多目标学习与不确定性建模相结合，同时输出流量质量预测和可靠的置信度估计。我们进一步对UMDA应用知识蒸馏，使模型能够通过单次前向传播同时产生偶然不确定性和认知不确定性，从而大幅降低不确定性建模的计算开销，同时基本保持预测精度并保留多次前向传播不确定性估计的优势。在JD和Criteo数据集上的实验表明，UMDA通过不确定性共享为下游任务提供更有效的样本，而蒸馏模型在保持原始不确定性共享能力的同时，推理速度提升了一个数量级。