# Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising

arXiv: https://arxiv.org/abs/1305.3011 · HTML: https://mlsanity.com/paper/1305.3011/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2013.

## Built on by (most cited first, 13 of 13)

- [Optimal Regularized Online Allocation by Adaptive Re-Solving](https://mlsanity.com/paper/2209.00399/) (2022)
- [Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets](https://mlsanity.com/paper/2202.05881/) (2022)
- [Dynamic Budget Throttling in Repeated Second-Price Auctions](https://mlsanity.com/paper/2207.04690/) (2022)
- [Online Bidding under RoS Constraints without Knowing the Value](https://mlsanity.com/paper/2503.03195/) (2025)
- [BAT: Benchmark for Auto-bidding Task](https://mlsanity.com/paper/2505.08485/) (2025)
- [Personalized Interpolation: Achieving Efficient Conversion Estimation with Flexible Optimization Windows](https://mlsanity.com/paper/2501.14103/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [Functional Optimization Reinforcement Learning for Real-Time Bidding](https://mlsanity.com/paper/2206.13939/) (2022)
- [A Profit-Maximizing Strategy for Advertising on the e-Commerce Platforms](https://mlsanity.com/paper/2211.01160/) (2022)
- [Online Non-Stationary Stochastic Quasar-Convex Optimization](https://mlsanity.com/paper/2407.03601/) (2024)
- [Plan-Driven Adaptive Bidding for First-Price Auctions with Budget Constraints under Nonstationarity](https://mlsanity.com/paper/2505.02796/) (2025)
- [DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation](https://mlsanity.com/paper/2606.14192/) (2026)
- [When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions](https://mlsanity.com/paper/2609.33312/) (2026)

## Abstract (arXiv)

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.