# Bid Shading in The Brave New World of First-Price Auctions

arXiv: https://arxiv.org/abs/2009.01360 · HTML: https://mlsanity.com/paper/2009.01360/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [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)
- [Generative Bid Shading in Real-Time Bidding Advertising](https://mlsanity.com/paper/2508.06550/) (2025)
- [Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search](https://mlsanity.com/paper/2509.15927/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [Optimizing Floors in First Price Auctions: an Empirical Study of Yahoo Advertising](https://mlsanity.com/paper/2302.06018/) (2023)
- [Learning Concave Bid Shading Strategies in Online Auctions via Measure-valued Proximal Optimization](https://mlsanity.com/paper/2509.10693/) (2025)
- [HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments](https://mlsanity.com/paper/2510.15238/) (2025)
- [The Spectral Neuron](https://mlsanity.com/paper/2608.08003/) (2026)

## Abstract (arXiv)

Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth. With great changes in how auctions are being organized, such as changing the second- to first-price auction type, advertisers and demand platforms are compelled to adapt to a new volatile environment. Bid shading is a known technique for preventing overpaying in auction systems that can help maintain the strategy equilibrium in first-price auctions, tackling one of its greatest drawbacks. In this study, we propose a machine learning approach of modeling optimal bid shading for non-censored online first-price ad auctions. We clearly motivate the approach and extensively evaluate it in both offline and online settings on a major demand side platform. The results demonstrate the superiority and robustness of the new approach as compared to the existing approaches across a range of performance metrics.