# Online Ad Procurement in Non-stationary Autobidding Worlds

arXiv: https://arxiv.org/abs/2307.05698 · HTML: https://mlsanity.com/paper/2307.05698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Linear Stochastic Bandits Under Safety Constraints](https://mlsanity.com/paper/1908.05814/) (2019)
- [Adaptive Algorithms for Online Convex Optimization with Long-term Constraints](https://mlsanity.com/paper/1512.07422/) (2015)
- [The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems](https://mlsanity.com/paper/2011.10124/) (2020)
- [Online convex optimization for cumulative constraints](https://mlsanity.com/paper/1802.06472/) (2018)
- [Selling to a No-Regret Buyer](https://mlsanity.com/paper/1711.09176/) (2017)
- [Bandit Convex Optimization in Non-stationary Environments](https://mlsanity.com/paper/1907.12340/) (2019)
- [A Unifying Framework for Online Optimization with Long-Term Constraints](https://mlsanity.com/paper/2209.07454/) (2022)
- [Online learning in repeated auctions](https://mlsanity.com/paper/1511.05720/) (2015)
- [Optimal No-regret Learning in Repeated First-price Auctions](https://mlsanity.com/paper/2003.09795/) (2020)
- [Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions](https://mlsanity.com/paper/2007.04568/) (2020)
- [Robust Auction Design in the Auto-bidding World](https://mlsanity.com/paper/2111.02468/) (2021)
- [An optimal algorithm for bandit convex optimization](https://mlsanity.com/paper/1603.04350/) (2016)
- [Towards Efficient Auctions in an Auto-bidding World](https://mlsanity.com/paper/2103.13356/) (2021)
- [Projection-Free Bandit Convex Optimization](https://mlsanity.com/paper/1805.07474/) (2018)
- [Online Bidding Algorithms for Return-on-Spend Constrained Advertisers](https://mlsanity.com/paper/2208.13713/) (2022)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [Incentive-aware Contextual Pricing with Non-parametric Market Noise](https://mlsanity.com/paper/1911.03508/) (2019)
- [Safe Online Convex Optimization with Unknown Linear Safety Constraints](https://mlsanity.com/paper/2111.07430/) (2021)
- [Learning Product Rankings Robust to Fake Users](https://mlsanity.com/paper/2009.05138/) (2020)
- [Fair Assortment Planning](https://mlsanity.com/paper/2208.07341/) (2022)
- [Auction Design in an Auto-bidding Setting: Randomization Improves Efficiency Beyond VCG](https://mlsanity.com/paper/2204.10956/) (2022)
- [Efficiency of the First-Price Auction in the Autobidding World](https://mlsanity.com/paper/2208.10650/) (2022)
- [No-regret Learning in Price Competitions under Consumer Reference Effects](https://mlsanity.com/paper/2011.03653/) (2020)

## Built on by (most cited first, 1 of 1)

- [Knowledge-informed Bidding with Dual-process Control for Online Advertising](https://mlsanity.com/paper/2603.04920/) (2026)

## Abstract (arXiv)

Today's online advertisers procure digital ad impressions through interacting with autobidding platforms: advertisers convey high level procurement goals via setting levers such as budget, target return-on-investment, max cost per click, etc.. Then ads platforms subsequently procure impressions on advertisers' behalf, and report final procurement conversions (e.g. click) to advertisers. In practice, advertisers may receive minimal information on platforms' procurement details, and procurement outcomes are subject to non-stationary factors like seasonal patterns, occasional system corruptions, and market trends which make it difficult for advertisers to optimize lever decisions effectively. Motivated by this, we present an online learning framework that helps advertisers dynamically optimize ad platform lever decisions while subject to general long-term constraints in a realistic bandit feedback environment with non-stationary procurement outcomes. In particular, we introduce a primal-dual algorithm for online decision making with multi-dimension decision variables, bandit feedback and long-term uncertain constraints. We show that our algorithm achieves low regret in many worlds when procurement outcomes are generated through procedures that are stochastic, adversarial, adversarially corrupted, periodic, and ergodic, respectively, without having to know which procedure is the ground truth. Finally, we emphasize that our proposed algorithm and theoretical results extend beyond the applications of online advertising.