# An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

arXiv: https://arxiv.org/abs/2107.06650 · HTML: https://mlsanity.com/paper/2107.06650/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://mlsanity.com/paper/1806.03514/) (2018)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising](https://mlsanity.com/paper/1907.10235/) (2019)
- [Bid Shading in The Brave New World of First-Price Auctions](https://mlsanity.com/paper/2009.01360/) (2020)
- [Bid Shading by Win-Rate Estimation and Surplus Maximization](https://mlsanity.com/paper/2009.09259/) (2020)

## Built on by (most cited first, 14 of 14)

- [An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising](https://mlsanity.com/paper/2502.05187/) (2025)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning](https://mlsanity.com/paper/2210.07770/) (2022)
- [HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2312.17503/) (2023)
- [Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising](https://mlsanity.com/paper/2110.13587/) (2021)
- [MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising](https://mlsanity.com/paper/2403.02607/) (2024)
- [Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)](https://mlsanity.com/paper/2412.11434/) (2024)
- [Randomized algorithms for precise measurement of differentially-private, personalized recommendations](https://mlsanity.com/paper/2308.03735/) (2023)
- [Generative Bid Shading in Real-Time Bidding Advertising](https://mlsanity.com/paper/2508.06550/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [RTB Formulation Using Point Process](https://mlsanity.com/paper/2308.09122/) (2023)
- [HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments](https://mlsanity.com/paper/2510.15238/) (2025)
- [Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion](https://mlsanity.com/paper/2601.20422/) (2026)
- [The Spectral Neuron](https://mlsanity.com/paper/2608.08003/) (2026)

## Abstract (arXiv)

Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference between these auctions, demand-side platforms (DSPs) have had to update their bidding strategies to avoid bidding unnecessarily high and hence overpaying. Bid shading was proposed to adjust the bid price intended for second-price auctions, in order to balance cost and winning probability in a first-price auction setup. In this study, we introduce a novel deep distribution network for optimal bidding in both open (non-censored) and closed (censored) online first-price auctions. Offline and online A/B testing results show that our algorithm outperforms previous state-of-art algorithms in terms of both surplus and effective cost per action (eCPX) metrics. Furthermore, the algorithm is optimized in run-time and has been deployed into VerizonMedia DSP as production algorithm, serving hundreds of billions of bid requests per day. Online A/B test shows that advertiser's ROI are improved by +2.4%, +2.4%, and +8.6% for impression based (CPM), click based (CPC), and conversion based (CPA) campaigns respectively.