# Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

arXiv: https://arxiv.org/abs/1803.02194 · HTML: https://mlsanity.com/paper/1803.02194/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 21 of 21)

- [Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning](https://mlsanity.com/paper/2305.06784/) (2023)
- [We Know What You Want: An Advertising Strategy Recommender System for Online Advertising](https://mlsanity.com/paper/2105.14188/) (2021)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [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)
- [Differentially Private AUC Computation in Vertical Federated Learning](https://mlsanity.com/paper/2205.12412/) (2022)
- [Multi-Session Budget Optimization for Forward Auction-based Federated Learning](https://mlsanity.com/paper/2311.12548/) (2023)
- [Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)](https://mlsanity.com/paper/2412.11434/) (2024)
- [Bid Optimization using Maximum Entropy Reinforcement Learning](https://mlsanity.com/paper/2110.05032/) (2021)
- [Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks](https://mlsanity.com/paper/2204.11588/) (2022)
- [Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm](https://mlsanity.com/paper/2211.05495/) (2022)
- [Generative Bid Shading in Real-Time Bidding Advertising](https://mlsanity.com/paper/2508.06550/) (2025)
- [Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lens](https://mlsanity.com/paper/2510.23410/) (2025)
- [PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective](https://mlsanity.com/paper/2607.27265/) (2026)
- [Functional Optimization Reinforcement Learning for Real-Time Bidding](https://mlsanity.com/paper/2206.13939/) (2022)
- [Risk-Aware Bid Optimization for Online Display Advertisement](https://mlsanity.com/paper/2210.15837/) (2022)
- [Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model](https://mlsanity.com/paper/2302.03332/) (2023)
- [Machina Economicus: A New Paradigm for Prosumers in the Energy Internet of Smart Cities](https://mlsanity.com/paper/2403.14660/) (2024)
- [Neural Optimization with Adaptive Heuristics for Intelligent Marketing System](https://mlsanity.com/paper/2405.10490/) (2024)
- [Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards](https://mlsanity.com/paper/2510.20055/) (2025)
- [OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios](https://mlsanity.com/paper/2609.21550/) (2026)

## Abstract (arXiv)

Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.