Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration

תקציר מקורי באנגליתarXiv:2511.05582v3 Announce Type: replace Abstract: Real-Time Auction (RTA) interception decides which incoming advertising requests reach downstream systems, and therefore controls the quality of the data those systems learn from. At JD.com, off-site advertising produces on the order of hundreds of billions of requests per day, and the RTA channel alone serves up to hundreds of millions of requests per minute. Filtering low-quality and fraudulent traffic at this scale requires estimating each request's value with calibrated confidence, which we treat as an uncertainty modeling problem. Two obstacles stand in the way. First, advertising labels are severely imbalanced: deals are rare, and we show both analytically and empirically that standard weight-based uncertainty degrades under such sp