# Bidding Agent Design in the LinkedIn Ad Marketplace

arXiv: https://arxiv.org/abs/2202.12472 · HTML: https://mlsanity.com/paper/2202.12472/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems](https://mlsanity.com/paper/2011.10124/) (2020)
- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [Smart Pacing for Effective Online Ad Campaign Optimization](https://mlsanity.com/paper/1506.05851/) (2015)
- [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)

## Built on by (most cited first, 7 of 7)

- [Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics](https://mlsanity.com/paper/2301.13306/) (2023)
- [Online Bidding Algorithms for Return-on-Spend Constrained Advertisers](https://mlsanity.com/paper/2208.13713/) (2022)
- [Online Bidding under RoS Constraints without Knowing the Value](https://mlsanity.com/paper/2503.03195/) (2025)
- [Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising](https://mlsanity.com/paper/2304.00999/) (2023)
- [RTB Formulation Using Point Process](https://mlsanity.com/paper/2308.09122/) (2023)
- [Cost-Control in Display Advertising: Theory vs Practice](https://mlsanity.com/paper/2409.03874/) (2024)
- [A Lightweight MPC Bidding Framework for Brand Auction Ads](https://mlsanity.com/paper/2603.07721/) (2026)

## Abstract (arXiv)

We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces. It optimizes solely for the buyer's interest and is agnostic to the auction mechanism imposed by the seller. As a result, the framework allows, for instance, the joint optimization of a group of ads across multiple platforms each running its own auction format. Bidding strategy derived from this framework automatically guarantees the optimality of budget allocation across ad units and platforms. Common constraints such as budget delivery schedule, return on investments and guaranteed results, directly translates to additional parameters in the bidding formula. We share practical learnings of the deployed bidding system in the LinkedIn ad marketplace based on this framework.