# Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration

arXiv: https://arxiv.org/abs/2511.05582 · HTML: https://mlsanity.com/paper/2511.05582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 19 of 19)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [A Simple Baseline for Bayesian Uncertainty in Deep Learning](https://mlsanity.com/paper/1902.02476/) (2019)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Deep Deterministic Uncertainty: A Simple Baseline](https://mlsanity.com/paper/2102.11582/) (2021)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Embedding-based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2106.09297/) (2021)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks](https://mlsanity.com/paper/2203.06801/) (2022)
- [Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets](https://mlsanity.com/paper/2207.05471/) (2022)
- [AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning](https://mlsanity.com/paper/2110.13076/) (2021)
- [UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation](https://mlsanity.com/paper/2201.08024/) (2022)
- [Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling](https://mlsanity.com/paper/2106.07356/) (2021)
- [Efficient Multi-Task Learning via Generalist Recommender](https://mlsanity.com/paper/2504.05318/) (2025)

## Abstract (arXiv)

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 sparsity, collapsing onto predicted probability and adding no signal. Second, methods such as SWAG and Bayesian neural networks require multiple stochastic forward passes per request, making full-traffic scoring prohibitively expensive. We address both problems with UMDA, a multi-objective framework that shares uncertainty across funnel-correlated objectives, using the reliable uncertainty of a balanced metric to compensate for the degenerate uncertainty of sparse ones. We then distill the multi-pass teacher into a single-pass student that reproduces both aleatoric and epistemic uncertainty at roughly one-tenth of the inference cost. On JD e-commerce dataset and the public Criteo dataset, UMDA supplies more effective samples to downstream tasks, and the distilled student preserves this capability. In production, it scores the full traffic in a near-line pipeline that feeds an hourly blacklist for online interception; a seven-day A/B test on 5% of live traffic cuts the click fraud rate by 3.59% and raises CVR by 4.01% at a matched interception ratio while leaving converted users essentially unchanged, and the model has since been deployed to full traffic.