Distillation-Accelerated Uncertainty Modeling for Multi-Objective RTA Interception

Department of Applied Mathematics Harbin Institute of T echnology, W eihai Weihai, China gaoxiang.zhao@stu.hit.edu.cn Abstract--Real-Time Auction (RT A) 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. T o address these challenges, we propose DAUM, a joint modeling framework that integrates multi-objective learning with uncertainty modeling, yielding both traffic quality predictions and reliable confidence estimates. Building on DAUM, we further apply knowledge distillation to reduce the computational overhead of uncertainty modeling, while largely preserving predictive accuracy and retaining the benefits of uncertainty estimation. Experiments on the JD advertisement dataset demonstrate that DAUM consistently improves predictive performance, with the distilled model delivering a tenfold increase in inference speed. In online advertising, RT A mechanisms play a central role in determining which traffic are exposed to downstream systems. Since not all incoming traffic contributes equally to campaign performance, an effective interception process is needed to filter out unproductive requests while preserving those that align with predefined objectives. Achieving this goal is particularly challenging because it requires not only the accurate prediction of multiple user-behavior metrics but also dependable estimates of prediction confidence under highly dynamic conditions. A natural way to address these requirements is to combine multi-objective optimization with uncertainty modeling.