# A Lightweight MPC Bidding Framework for Brand Auction Ads

arXiv: https://arxiv.org/abs/2603.07721 · HTML: https://mlsanity.com/paper/2603.07721/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 8 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [Multiplicative Pacing Equilibria in Auction Markets](https://mlsanity.com/paper/1706.07151/) (2017)
- [Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns](https://mlsanity.com/paper/2003.01452/) (2020)
- [Smart Pacing for Effective Online Ad Campaign Optimization](https://mlsanity.com/paper/1506.05851/) (2015)
- [ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning](https://mlsanity.com/paper/2206.05240/) (2022)
- [Profit Maximization for Online Advertising Demand-Side Platforms](https://mlsanity.com/paper/1706.01614/) (2017)
- [Optimal Real-Time Bidding Strategies](https://mlsanity.com/paper/1511.08409/) (2015)
- [Bidding Agent Design in the LinkedIn Ad Marketplace](https://mlsanity.com/paper/2202.12472/) (2022)
- [Dynamic Bidding Strategies with Multivariate Feedback Control for Multiple Goals in Display Advertising](https://mlsanity.com/paper/2007.00426/) (2020)
- [Analysis of Dual-Based PID Controllers through Convolutional Mirror Descent](https://mlsanity.com/paper/2202.06152/) (2022)
- [Percentile Risk-Constrained Budget Pacing for Guaranteed Display Advertising in Online Optimization](https://mlsanity.com/paper/2312.06174/) (2023)
- [A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising](https://mlsanity.com/paper/1405.5189/) (2014)
- [Impression Allocation and Policy Search in Display Advertising](https://mlsanity.com/paper/2203.07073/) (2022)
- [A Practical Guide to Budget Pacing Algorithms in Digital Advertising](https://mlsanity.com/paper/2503.06942/) (2025)

## Abstract (arXiv)

Brand advertising plays a critical role in building long-term consumer awareness and loyalty, making it a key objective for advertisers across digital platforms. Although real-time bidding has been extensively studied, there is limited literature on algorithms specifically tailored for brand auction ads that fully leverage their unique characteristics. In this paper, we propose a lightweight Model Predictive Control (MPC) framework designed for brand advertising campaigns, exploiting the inherent attributes of brand ads -- such as stable user engagement patterns and fast feedback loops -- to simplify modeling and improve efficiency. Our approach utilizes online isotonic regression to construct monotonic bid-to-spend and bid-to-conversion models directly from streaming data, eliminating the need for complex machine learning models. The algorithm operates fully online with low computational overhead, making it highly practical for real-world deployment. Simulation results demonstrate that our approach significantly improves spend efficiency and cost control compared to baseline strategies, providing a scalable and easily implementable solution for modern brand advertising platforms.