# Multi-Platform Budget Management in Ad Markets with Non-IC Auctions

arXiv: https://arxiv.org/abs/2306.07352 · HTML: https://mlsanity.com/paper/2306.07352/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions](https://mlsanity.com/paper/2002.11137/) (2020)
- [Optimal No-regret Learning in Repeated First-price Auctions](https://mlsanity.com/paper/2003.09795/) (2020)
- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [Learning Simple Auctions](https://mlsanity.com/paper/1604.03171/) (2016)
- [Learning Multi-item Auctions with (or without) Samples](https://mlsanity.com/paper/1709.00228/) (2017)
- [Multiplicative Pacing Equilibria in Auction Markets](https://mlsanity.com/paper/1706.07151/) (2017)
- [Pacing Equilibrium in First-Price Auction Markets](https://mlsanity.com/paper/1811.07166/) (2018)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [Ironing in the Dark](https://mlsanity.com/paper/1511.06918/) (2015)
- [Incentive-aware Contextual Pricing with Non-parametric Market Noise](https://mlsanity.com/paper/1911.03508/) (2019)
- [Efficiency of the First-Price Auction in the Autobidding World](https://mlsanity.com/paper/2208.10650/) (2022)
- [Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees](https://mlsanity.com/paper/2102.10476/) (2021)
- [Dynamic Reserve Prices for Repeated Auctions: Learning from Bids](https://mlsanity.com/paper/2002.07331/) (2020)
- [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)
- [Price Manipulability in First-Price Auctions](https://mlsanity.com/paper/2203.12042/) (2022)
- [Efficiency of non-truthful auctions under auto-bidding](https://mlsanity.com/paper/2207.03630/) (2022)

## Built on by (most cited first, 3 of 3)

- [Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement](https://mlsanity.com/paper/2506.21956/) (2025)
- [Learning in Repeated Multi-Unit Pay-As-Bid Auctions](https://mlsanity.com/paper/2307.15193/) (2023)
- [HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments](https://mlsanity.com/paper/2510.15238/) (2025)

## Abstract (arXiv)

In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation. Additionally, we investigate the online setting where the advertiser must submit bids across platforms while learning about other bidders' bids over time. Our algorithm has O(T3/4) regret under the full-information setting. Finally, we demonstrate that our algorithms have superior cumulative regret on both synthetic and real-world datasets of ad placement auctions, compared to existing adaptive pacing algorithms.