Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding
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Abstract (arXiv)
In this work, we present a scalable and efficient system for exploring the supply landscape in real-time bidding. The system directs exploration based on the predictive uncertainty of models used for click-through rate prediction and works in a high-throughput, low-latency environment. Through online A/B testing, we demonstrate that exploration with model uncertainty has a positive impact on model performance and business KPIs.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 3 Aug 2022.
Builds on
9 of 14 references.
References · 9
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
- 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
- 2016Wide & Deep Learning for Recommender Systemscited by 745
- 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
- 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
- 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
- 2016Display Advertising with Real-Time Bidding (RTB) and Behavioural Targetingcited by 42
- 2021Scaling TensorFlow to 300 million predictions per secondcited by 2
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Citing papers per year: 2023: 1.
1 paper.