# Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns

arXiv: https://arxiv.org/abs/2003.01452 · HTML: https://mlsanity.com/paper/2003.01452/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising](https://mlsanity.com/paper/2502.05187/) (2025)
- [Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets](https://mlsanity.com/paper/2202.05881/) (2022)
- [Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching](https://mlsanity.com/paper/2210.15459/) (2022)
- [DARA: Few-shot Budget Allocation in Online Advertising via In-Context Decision Making with RL-Finetuned LLMs](https://mlsanity.com/paper/2601.14711/) (2026)
- [Safe Online Bid Optimization with Return on Investment and Budget Constraints](https://mlsanity.com/paper/2201.07139/) (2022)
- [Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processes](https://mlsanity.com/paper/2110.02248/) (2021)
- [Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts](https://mlsanity.com/paper/2206.00586/) (2022)
- [Multi-Task Combinatorial Bandits for Budget Allocation](https://mlsanity.com/paper/2409.00561/) (2024)
- [Note on Follow-the-Perturbed-Leader in Combinatorial Semi-Bandit Problems](https://mlsanity.com/paper/2506.12490/) (2025)
- [FALSE: Fake News Automatic and Lightweight Solution](https://mlsanity.com/paper/2208.07686/) (2022)
- [Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making](https://mlsanity.com/paper/2210.08486/) (2022)
- [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)
- [AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising](https://mlsanity.com/paper/2602.22650/) (2026)
- [A Lightweight MPC Bidding Framework for Brand Auction Ads](https://mlsanity.com/paper/2603.07721/) (2026)

## Abstract (arXiv)

Pay-per-click advertising includes various formats (e.g., search, contextual, social) with a total investment of more than 200 billion USD per year worldwide. An advertiser is given a daily budget to allocate over several, even thousands, campaigns, mainly distinguishing for the ad, target, or channel. Furthermore, publishers choose the ads to display and how to allocate them employing auctioning mechanisms, in which every day the advertisers set for each campaign a bid corresponding to the maximum amount of money per click they are willing to pay and the fraction of the daily budget to invest. In this paper, we study the problem of automating the online joint bid/daily budget optimization of pay-per-click advertising campaigns over multiple channels. We formulate our problem as a combinatorial semi-bandit problem, which requires solving a special case of the Multiple-Choice Knapsack problem every day. Furthermore, for every campaign, we capture the dependency of the number of clicks on the bid and daily budget by Gaussian Processes, thus requiring mild assumptions on the regularity of these functions. We design four algorithms and show that they suffer from a regret that is upper bounded with high probability as O(sqrtT), where T is the time horizon of the learning process. We experimentally evaluate our algorithms with synthetic settings generated from real data from Yahoo!, and we present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1,000 Euros for more than one year.